Overview

Dataset statistics

Number of variables83
Number of observations3086
Missing cells226238
Missing cells (%)88.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.0 MiB
Average record size in memory664.0 B

Variable types

Categorical47
Numeric36

Alerts

0,375 Mbps has constant value "225.0"Constant
0,625 Mbps has constant value "111.0"Constant
1,25 Mbps has constant value "25.0"Constant
2,2 Mbps has constant value "26.0"Constant
3,3 Mbps has constant value "6.0"Constant
4,5 Mbps has constant value "65.0"Constant
6,4 Mbps has constant value "13.0"Constant
17 Mbps has constant value "2.0"Constant
19 Mbps has constant value "1.0"Constant
22 Mbps has constant value "1.0"Constant
25,1 Mbps has constant value "5.0"Constant
25,11 Mbps has constant value "1.0"Constant
25,5 Mbps has constant value "35.0"Constant
34 Mbps has constant value "2.0"Constant
38 Mbps has constant value "4.0"Constant
39 Mbps has constant value "60.0"Constant
41 Mbps has constant value "9.0"Constant
46 Mbps has constant value "6.0"Constant
49 Mbps has constant value "1.0"Constant
58 Mbps has constant value "2.0"Constant
59 Mbps has constant value "59.0"Constant
62 Mbps has constant value "2.0"Constant
64 Mbps has constant value "8.0"Constant
66 Mbps has constant value "1.0"Constant
78 Mbps has constant value "4.0"Constant
82 Mbps has constant value "1.0"Constant
83 Mbps has constant value "2.0"Constant
85 Mbps has constant value "14.0"Constant
95 Mbps has constant value "1.0"Constant
Partido has a high cardinality: 431 distinct valuesHigh cardinality
Localidad has a high cardinality: 2786 distinct valuesHigh cardinality
Link Indec has a high cardinality: 2656 distinct valuesHigh cardinality
Otros has 2127 (68.9%) missing valuesMissing
0,256 Mbps has 3057 (99.1%) missing valuesMissing
0,375 Mbps has 3085 (> 99.9%) missing valuesMissing
0,5 Mbps has 2417 (78.3%) missing valuesMissing
0,512 Mbps has 2562 (83.0%) missing valuesMissing
0,625 Mbps has 3085 (> 99.9%) missing valuesMissing
0,75 Mbps has 2770 (89.8%) missing valuesMissing
1 Mbps has 2138 (69.3%) missing valuesMissing
1,25 Mbps has 3085 (> 99.9%) missing valuesMissing
1,5 Mbps has 3070 (99.5%) missing valuesMissing
2 Mbps has 2318 (75.1%) missing valuesMissing
2,2 Mbps has 3085 (> 99.9%) missing valuesMissing
2,5 Mbps has 3083 (99.9%) missing valuesMissing
3 Mbps has 1712 (55.5%) missing valuesMissing
3,3 Mbps has 3085 (> 99.9%) missing valuesMissing
3,5 Mbps has 2703 (87.6%) missing valuesMissing
4 Mbps has 2544 (82.4%) missing valuesMissing
4,5 Mbps has 3085 (> 99.9%) missing valuesMissing
5 Mbps has 2096 (67.9%) missing valuesMissing
6 Mbps has 1635 (53.0%) missing valuesMissing
6,4 Mbps has 3085 (> 99.9%) missing valuesMissing
7 Mbps has 2823 (91.5%) missing valuesMissing
7,5 Mbps has 3082 (99.9%) missing valuesMissing
8 Mbps has 2408 (78.0%) missing valuesMissing
9 Mbps has 3034 (98.3%) missing valuesMissing
10 Mbps has 1426 (46.2%) missing valuesMissing
11 Mbps has 3059 (99.1%) missing valuesMissing
12 Mbps has 2574 (83.4%) missing valuesMissing
13 Mbps has 3071 (99.5%) missing valuesMissing
14 Mbps has 3066 (99.4%) missing valuesMissing
15 Mbps has 2078 (67.3%) missing valuesMissing
16 Mbps has 3068 (99.4%) missing valuesMissing
17 Mbps has 3085 (> 99.9%) missing valuesMissing
18 Mbps has 2854 (92.5%) missing valuesMissing
19 Mbps has 3085 (> 99.9%) missing valuesMissing
20 Mbps has 2139 (69.3%) missing valuesMissing
21 Mbps has 3082 (99.9%) missing valuesMissing
22 Mbps has 3085 (> 99.9%) missing valuesMissing
23 Mbps has 3084 (99.9%) missing valuesMissing
24 Mbps has 3073 (99.6%) missing valuesMissing
25 Mbps has 2502 (81.1%) missing valuesMissing
25,1 Mbps has 3085 (> 99.9%) missing valuesMissing
25,11 Mbps has 3085 (> 99.9%) missing valuesMissing
25,5 Mbps has 3085 (> 99.9%) missing valuesMissing
26 Mbps has 3084 (99.9%) missing valuesMissing
30 Mbps has 2350 (76.2%) missing valuesMissing
31 Mbps has 3082 (99.9%) missing valuesMissing
32 Mbps has 3084 (99.9%) missing valuesMissing
34 Mbps has 3085 (> 99.9%) missing valuesMissing
35 Mbps has 3075 (99.6%) missing valuesMissing
36 Mbps has 3084 (99.9%) missing valuesMissing
38 Mbps has 3085 (> 99.9%) missing valuesMissing
39 Mbps has 3085 (> 99.9%) missing valuesMissing
40 Mbps has 2997 (97.1%) missing valuesMissing
41 Mbps has 3085 (> 99.9%) missing valuesMissing
45 Mbps has 3083 (99.9%) missing valuesMissing
46 Mbps has 3085 (> 99.9%) missing valuesMissing
49 Mbps has 3085 (> 99.9%) missing valuesMissing
50 Mbps has 2475 (80.2%) missing valuesMissing
55 Mbps has 3082 (99.9%) missing valuesMissing
58 Mbps has 3085 (> 99.9%) missing valuesMissing
59 Mbps has 3085 (> 99.9%) missing valuesMissing
60 Mbps has 2831 (91.7%) missing valuesMissing
61 Mbps has 3083 (99.9%) missing valuesMissing
62 Mbps has 3085 (> 99.9%) missing valuesMissing
64 Mbps has 3085 (> 99.9%) missing valuesMissing
65 Mbps has 3084 (99.9%) missing valuesMissing
66 Mbps has 3085 (> 99.9%) missing valuesMissing
70 Mbps has 3076 (99.7%) missing valuesMissing
75 Mbps has 2861 (92.7%) missing valuesMissing
78 Mbps has 3085 (> 99.9%) missing valuesMissing
80 Mbps has 3075 (99.6%) missing valuesMissing
82 Mbps has 3085 (> 99.9%) missing valuesMissing
83 Mbps has 3085 (> 99.9%) missing valuesMissing
85 Mbps has 3085 (> 99.9%) missing valuesMissing
90 Mbps has 3084 (99.9%) missing valuesMissing
92 Mbps has 3084 (99.9%) missing valuesMissing
95 Mbps has 3085 (> 99.9%) missing valuesMissing
100 Mbps has 2544 (82.4%) missing valuesMissing
0,5 Mbps is highly skewed (γ1 = 25.85045317)Skewed
2,5 Mbps is uniformly distributedUniform
7,5 Mbps is uniformly distributedUniform
23 Mbps is uniformly distributedUniform
26 Mbps is uniformly distributedUniform
31 Mbps is uniformly distributedUniform
32 Mbps is uniformly distributedUniform
36 Mbps is uniformly distributedUniform
45 Mbps is uniformly distributedUniform
61 Mbps is uniformly distributedUniform
65 Mbps is uniformly distributedUniform
90 Mbps is uniformly distributedUniform
92 Mbps is uniformly distributedUniform

Reproduction

Analysis started2023-01-03 16:53:23.883718
Analysis finished2023-01-03 16:55:00.124233
Duration1 minute and 36.24 seconds
Software versionpandas-profiling vv3.6.2
Download configurationconfig.json

Variables

Provincia
Categorical

Distinct24
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size24.2 KiB
BUENOS AIRES
707 
CORDOBA
382 
SANTA FE
350 
MENDOZA
150 
ENTRE RIOS
148 
Other values (19)
1349 

Length

Max length19
Median length12
Mean length9.0771225
Min length4

Characters and Unicode

Total characters28012
Distinct characters23
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowBUENOS AIRES
2nd rowBUENOS AIRES
3rd rowBUENOS AIRES
4th rowBUENOS AIRES
5th rowBUENOS AIRES

Common Values

ValueCountFrequency (%)
BUENOS AIRES 707
22.9%
CORDOBA 382
12.4%
SANTA FE 350
11.3%
MENDOZA 150
 
4.9%
ENTRE RIOS 148
 
4.8%
SANTIAGO DEL ESTERO 137
 
4.4%
SALTA 122
 
4.0%
SAN LUIS 97
 
3.1%
MISIONES 94
 
3.0%
RIO NEGRO 92
 
3.0%
Other values (14) 807
26.2%

Length

2023-01-03T11:55:00.172887image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
buenos 707
14.1%
aires 707
14.1%
cordoba 382
 
7.6%
santa 372
 
7.4%
fe 350
 
7.0%
san 176
 
3.5%
mendoza 150
 
3.0%
la 150
 
3.0%
entre 148
 
2.9%
rios 148
 
2.9%
Other values (23) 1731
34.5%

Most occurring characters

ValueCountFrequency (%)
A 3637
13.0%
E 3065
10.9%
S 2919
10.4%
O 2665
9.5%
N 2191
7.8%
R 2077
 
7.4%
1935
 
6.9%
I 1511
 
5.4%
U 1457
 
5.2%
T 1190
 
4.2%
Other values (13) 5365
19.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 26077
93.1%
Space Separator 1935
 
6.9%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 3637
13.9%
E 3065
11.8%
S 2919
11.2%
O 2665
10.2%
N 2191
8.4%
R 2077
8.0%
I 1511
 
5.8%
U 1457
 
5.6%
T 1190
 
4.6%
B 1153
 
4.4%
Other values (12) 4212
16.2%
Space Separator
ValueCountFrequency (%)
1935
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 26077
93.1%
Common 1935
 
6.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 3637
13.9%
E 3065
11.8%
S 2919
11.2%
O 2665
10.2%
N 2191
8.4%
R 2077
8.0%
I 1511
 
5.8%
U 1457
 
5.6%
T 1190
 
4.6%
B 1153
 
4.4%
Other values (12) 4212
16.2%
Common
ValueCountFrequency (%)
1935
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28012
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 3637
13.0%
E 3065
10.9%
S 2919
10.4%
O 2665
9.5%
N 2191
7.8%
R 2077
 
7.4%
1935
 
6.9%
I 1511
 
5.4%
U 1457
 
5.2%
T 1190
 
4.2%
Other values (13) 5365
19.2%

Partido
Categorical

Distinct431
Distinct (%)14.0%
Missing0
Missing (%)0.0%
Memory size24.2 KiB
San Justo
 
54
General San Martín
 
41
Colón
 
38
Castellanos
 
38
San Martín
 
37
Other values (426)
2878 

Length

Max length31
Median length26
Mean length10.064809
Min length4

Characters and Unicode

Total characters31060
Distinct characters65
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique40 ?
Unique (%)1.3%

Sample

1st row25 de Mayo
2nd row25 de Mayo
3rd row25 de Mayo
4th row25 de Mayo
5th row25 de Mayo

Common Values

ValueCountFrequency (%)
San Justo 54
 
1.7%
General San Martín 41
 
1.3%
Colón 38
 
1.2%
Castellanos 38
 
1.2%
San Martín 37
 
1.2%
Capital 35
 
1.1%
La Capital 34
 
1.1%
San Javier 33
 
1.1%
Rivadavia 31
 
1.0%
Rosario 31
 
1.0%
Other values (421) 2714
87.9%

Length

2023-01-03T11:55:00.264163image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
san 404
 
8.0%
general 248
 
4.9%
de 182
 
3.6%
la 124
 
2.5%
martín 121
 
2.4%
río 83
 
1.7%
capital 69
 
1.4%
justo 57
 
1.1%
santa 56
 
1.1%
las 47
 
0.9%
Other values (484) 3637
72.3%

Most occurring characters

ValueCountFrequency (%)
a 4084
 
13.1%
e 2569
 
8.3%
n 2311
 
7.4%
r 2055
 
6.6%
o 2054
 
6.6%
1942
 
6.3%
l 1770
 
5.7%
i 1421
 
4.6%
s 1026
 
3.3%
t 993
 
3.2%
Other values (55) 10835
34.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 24072
77.5%
Uppercase Letter 4871
 
15.7%
Space Separator 1942
 
6.3%
Decimal Number 100
 
0.3%
Other Punctuation 73
 
0.2%
Other Letter 2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 4084
17.0%
e 2569
10.7%
n 2311
9.6%
r 2055
8.5%
o 2054
8.5%
l 1770
 
7.4%
i 1421
 
5.9%
s 1026
 
4.3%
t 993
 
4.1%
u 966
 
4.0%
Other values (23) 4823
20.0%
Uppercase Letter
ValueCountFrequency (%)
C 697
14.3%
S 583
12.0%
L 422
8.7%
G 402
8.3%
M 384
 
7.9%
P 350
 
7.2%
A 339
 
7.0%
R 298
 
6.1%
J 255
 
5.2%
B 181
 
3.7%
Other values (15) 960
19.7%
Decimal Number
ValueCountFrequency (%)
2 35
35.0%
9 30
30.0%
5 30
30.0%
1 5
 
5.0%
Space Separator
ValueCountFrequency (%)
1942
100.0%
Other Punctuation
ValueCountFrequency (%)
. 73
100.0%
Other Letter
ValueCountFrequency (%)
º 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 28945
93.2%
Common 2115
 
6.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 4084
14.1%
e 2569
 
8.9%
n 2311
 
8.0%
r 2055
 
7.1%
o 2054
 
7.1%
l 1770
 
6.1%
i 1421
 
4.9%
s 1026
 
3.5%
t 993
 
3.4%
u 966
 
3.3%
Other values (49) 9696
33.5%
Common
ValueCountFrequency (%)
1942
91.8%
. 73
 
3.5%
2 35
 
1.7%
9 30
 
1.4%
5 30
 
1.4%
1 5
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 29997
96.6%
None 1063
 
3.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 4084
13.6%
e 2569
 
8.6%
n 2311
 
7.7%
r 2055
 
6.9%
o 2054
 
6.8%
1942
 
6.5%
l 1770
 
5.9%
i 1421
 
4.7%
s 1026
 
3.4%
t 993
 
3.3%
Other values (45) 9772
32.6%
None
ValueCountFrequency (%)
í 320
30.1%
ó 265
24.9%
á 226
21.3%
é 92
 
8.7%
ú 78
 
7.3%
ñ 48
 
4.5%
ü 22
 
2.1%
Ñ 8
 
0.8%
º 2
 
0.2%
Í 2
 
0.2%

Localidad
Categorical

Distinct2786
Distinct (%)90.3%
Missing0
Missing (%)0.0%
Memory size24.2 KiB
OTROS
 
69
San Pedro
 
9
San Miguel
 
6
San José
 
6
San Antonio
 
6
Other values (2781)
2990 

Length

Max length54
Median length43
Mean length12.261504
Min length4

Characters and Unicode

Total characters37839
Distinct characters82
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2622 ?
Unique (%)85.0%

Sample

1st row25 de Mayo
2nd rowAgustín Mosconi
3rd rowDel Valle
4th rowErnestina
5th rowGobernador Ugarte

Common Values

ValueCountFrequency (%)
OTROS 69
 
2.2%
San Pedro 9
 
0.3%
San Miguel 6
 
0.2%
San José 6
 
0.2%
San Antonio 6
 
0.2%
Santa Ana 5
 
0.2%
Santa Rosa 5
 
0.2%
Lavalle 4
 
0.1%
Los Molles 4
 
0.1%
San Vicente 4
 
0.1%
Other values (2776) 2968
96.2%

Length

2023-01-03T11:55:00.358778image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
villa 244
 
4.0%
san 225
 
3.7%
de 189
 
3.1%
la 179
 
2.9%
el 151
 
2.5%
los 102
 
1.7%
del 96
 
1.6%
est 85
 
1.4%
las 84
 
1.4%
general 74
 
1.2%
Other values (2569) 4725
76.8%

Most occurring characters

ValueCountFrequency (%)
a 4986
 
13.2%
3112
 
8.2%
o 2667
 
7.0%
e 2638
 
7.0%
l 2437
 
6.4%
r 2342
 
6.2%
n 2170
 
5.7%
i 2040
 
5.4%
s 1493
 
3.9%
t 1130
 
3.0%
Other values (72) 12824
33.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 28153
74.4%
Uppercase Letter 6145
 
16.2%
Space Separator 3112
 
8.2%
Other Punctuation 140
 
0.4%
Close Punctuation 99
 
0.3%
Open Punctuation 99
 
0.3%
Decimal Number 63
 
0.2%
Dash Punctuation 25
 
0.1%
Other Letter 3
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 4986
17.7%
o 2667
9.5%
e 2638
9.4%
l 2437
8.7%
r 2342
8.3%
n 2170
7.7%
i 2040
 
7.2%
s 1493
 
5.3%
t 1130
 
4.0%
u 1079
 
3.8%
Other values (24) 5171
18.4%
Uppercase Letter
ValueCountFrequency (%)
C 711
11.6%
S 607
 
9.9%
L 571
 
9.3%
P 439
 
7.1%
A 422
 
6.9%
M 408
 
6.6%
V 399
 
6.5%
E 376
 
6.1%
R 306
 
5.0%
B 304
 
4.9%
Other values (21) 1602
26.1%
Decimal Number
ValueCountFrequency (%)
1 16
25.4%
2 15
23.8%
5 6
 
9.5%
3 6
 
9.5%
0 5
 
7.9%
9 5
 
7.9%
8 4
 
6.3%
4 3
 
4.8%
6 2
 
3.2%
7 1
 
1.6%
Other Punctuation
ValueCountFrequency (%)
. 135
96.4%
' 5
 
3.6%
Space Separator
ValueCountFrequency (%)
3112
100.0%
Close Punctuation
ValueCountFrequency (%)
) 99
100.0%
Open Punctuation
ValueCountFrequency (%)
( 99
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 25
100.0%
Other Letter
ValueCountFrequency (%)
º 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 34301
90.6%
Common 3538
 
9.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 4986
14.5%
o 2667
 
7.8%
e 2638
 
7.7%
l 2437
 
7.1%
r 2342
 
6.8%
n 2170
 
6.3%
i 2040
 
5.9%
s 1493
 
4.4%
t 1130
 
3.3%
u 1079
 
3.1%
Other values (56) 11319
33.0%
Common
ValueCountFrequency (%)
3112
88.0%
. 135
 
3.8%
) 99
 
2.8%
( 99
 
2.8%
- 25
 
0.7%
1 16
 
0.5%
2 15
 
0.4%
5 6
 
0.2%
3 6
 
0.2%
0 5
 
0.1%
Other values (6) 20
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 37041
97.9%
None 798
 
2.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 4986
13.5%
3112
 
8.4%
o 2667
 
7.2%
e 2638
 
7.1%
l 2437
 
6.6%
r 2342
 
6.3%
n 2170
 
5.9%
i 2040
 
5.5%
s 1493
 
4.0%
t 1130
 
3.1%
Other values (58) 12026
32.5%
None
ValueCountFrequency (%)
í 220
27.6%
ó 163
20.4%
á 156
19.5%
é 122
15.3%
ñ 78
 
9.8%
ú 40
 
5.0%
ü 6
 
0.8%
Á 4
 
0.5%
º 3
 
0.4%
Ñ 2
 
0.3%
Other values (4) 4
 
0.5%

Link Indec
Categorical

Distinct2656
Distinct (%)86.1%
Missing0
Missing (%)0.0%
Memory size24.2 KiB
Sin Datos
 
197
6441030
 
26
6371010
 
26
6840010
 
15
6427010
 
15
Other values (2651)
2807 

Length

Max length9
Median length8
Mean length7.8438108
Min length7

Characters and Unicode

Total characters24206
Distinct characters19
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2614 ?
Unique (%)84.7%

Sample

1st row6854100
2nd row6854010
3rd row6854020
4th row6854030
5th row6854040

Common Values

ValueCountFrequency (%)
Sin Datos 197
 
6.4%
6441030 26
 
0.8%
6371010 26
 
0.8%
6840010 15
 
0.5%
6427010 15
 
0.5%
6638040 13
 
0.4%
6028010 12
 
0.4%
6274010 11
 
0.4%
6091010 10
 
0.3%
6805010 9
 
0.3%
Other values (2646) 2752
89.2%

Length

2023-01-03T11:55:00.441424image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sin 197
 
6.0%
datos 197
 
6.0%
6441030 26
 
0.8%
6371010 26
 
0.8%
6840010 15
 
0.5%
6427010 15
 
0.5%
6638040 13
 
0.4%
6028010 12
 
0.4%
6274010 11
 
0.3%
6091010 10
 
0.3%
Other values (2647) 2761
84.1%

Most occurring characters

ValueCountFrequency (%)
0 7916
32.7%
1 2943
 
12.2%
2 2022
 
8.4%
6 2018
 
8.3%
4 1923
 
7.9%
8 1434
 
5.9%
3 1293
 
5.3%
5 1163
 
4.8%
7 1005
 
4.2%
9 716
 
3.0%
Other values (9) 1773
 
7.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 22433
92.7%
Lowercase Letter 1182
 
4.9%
Uppercase Letter 394
 
1.6%
Space Separator 197
 
0.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 7916
35.3%
1 2943
 
13.1%
2 2022
 
9.0%
6 2018
 
9.0%
4 1923
 
8.6%
8 1434
 
6.4%
3 1293
 
5.8%
5 1163
 
5.2%
7 1005
 
4.5%
9 716
 
3.2%
Lowercase Letter
ValueCountFrequency (%)
s 197
16.7%
i 197
16.7%
o 197
16.7%
t 197
16.7%
a 197
16.7%
n 197
16.7%
Uppercase Letter
ValueCountFrequency (%)
D 197
50.0%
S 197
50.0%
Space Separator
ValueCountFrequency (%)
197
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 22630
93.5%
Latin 1576
 
6.5%

Most frequent character per script

Common
ValueCountFrequency (%)
0 7916
35.0%
1 2943
 
13.0%
2 2022
 
8.9%
6 2018
 
8.9%
4 1923
 
8.5%
8 1434
 
6.3%
3 1293
 
5.7%
5 1163
 
5.1%
7 1005
 
4.4%
9 716
 
3.2%
Latin
ValueCountFrequency (%)
s 197
12.5%
i 197
12.5%
o 197
12.5%
t 197
12.5%
a 197
12.5%
D 197
12.5%
n 197
12.5%
S 197
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 24206
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 7916
32.7%
1 2943
 
12.2%
2 2022
 
8.4%
6 2018
 
8.3%
4 1923
 
7.9%
8 1434
 
5.9%
3 1293
 
5.3%
5 1163
 
4.8%
7 1005
 
4.2%
9 716
 
3.0%
Other values (9) 1773
 
7.3%

Otros
Real number (ℝ)

Distinct241
Distinct (%)25.1%
Missing2127
Missing (%)68.9%
Infinite0
Infinite (%)0.0%
Mean206.8123
Minimum-448
Maximum14756
Zeros27
Zeros (%)0.9%
Negative9
Negative (%)0.3%
Memory size24.2 KiB
2023-01-03T11:55:00.527739image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-448
5-th percentile1
Q11
median5
Q356
95-th percentile1034.7
Maximum14756
Range15204
Interquartile range (IQR)55

Descriptive statistics

Standard deviation1012.4128
Coefficient of variation (CV)4.8953221
Kurtosis134.91192
Mean206.8123
Median Absolute Deviation (MAD)4
Skewness10.673746
Sum198333
Variance1024979.8
MonotonicityNot monotonic
2023-01-03T11:55:00.611589image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 280
 
9.1%
2 78
 
2.5%
3 33
 
1.1%
4 29
 
0.9%
0 27
 
0.9%
5 25
 
0.8%
10 21
 
0.7%
30 16
 
0.5%
11 13
 
0.4%
15 12
 
0.4%
Other values (231) 425
 
13.8%
(Missing) 2127
68.9%
ValueCountFrequency (%)
-448 1
 
< 0.1%
-333 1
 
< 0.1%
-250 2
 
0.1%
-200 1
 
< 0.1%
-181 1
 
< 0.1%
-134 1
 
< 0.1%
-120 1
 
< 0.1%
-48 1
 
< 0.1%
0 27
 
0.9%
1 280
9.1%
ValueCountFrequency (%)
14756 1
< 0.1%
14400 1
< 0.1%
14182 1
< 0.1%
10064 1
< 0.1%
6978 1
< 0.1%
6266 1
< 0.1%
5618 1
< 0.1%
3540 1
< 0.1%
3199 1
< 0.1%
3032 1
< 0.1%

0,256 Mbps
Real number (ℝ)

Distinct7
Distinct (%)24.1%
Missing3057
Missing (%)99.1%
Infinite0
Infinite (%)0.0%
Mean3.3793103
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.2 KiB
2023-01-03T11:55:00.685399image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q33
95-th percentile14.4
Maximum31
Range30
Interquartile range (IQR)2

Descriptive statistics

Standard deviation6.411005
Coefficient of variation (CV)1.8971341
Kurtosis13.914517
Mean3.3793103
Median Absolute Deviation (MAD)0
Skewness3.7037923
Sum98
Variance41.100985
MonotonicityNot monotonic
2023-01-03T11:55:00.994815image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1 17
 
0.6%
2 4
 
0.1%
3 4
 
0.1%
31 1
 
< 0.1%
4 1
 
< 0.1%
6 1
 
< 0.1%
20 1
 
< 0.1%
(Missing) 3057
99.1%
ValueCountFrequency (%)
1 17
0.6%
2 4
 
0.1%
3 4
 
0.1%
4 1
 
< 0.1%
6 1
 
< 0.1%
20 1
 
< 0.1%
31 1
 
< 0.1%
ValueCountFrequency (%)
31 1
 
< 0.1%
20 1
 
< 0.1%
6 1
 
< 0.1%
4 1
 
< 0.1%
3 4
 
0.1%
2 4
 
0.1%
1 17
0.6%

0,375 Mbps
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing3085
Missing (%)> 99.9%
Memory size24.2 KiB
225.0

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters5
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row225.0

Common Values

ValueCountFrequency (%)
225.0 1
 
< 0.1%
(Missing) 3085
> 99.9%

Length

2023-01-03T11:55:01.062345image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-03T11:55:01.127904image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
225.0 1
100.0%

Most occurring characters

ValueCountFrequency (%)
2 2
40.0%
5 1
20.0%
. 1
20.0%
0 1
20.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4
80.0%
Other Punctuation 1
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 2
50.0%
5 1
25.0%
0 1
25.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 2
40.0%
5 1
20.0%
. 1
20.0%
0 1
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 2
40.0%
5 1
20.0%
. 1
20.0%
0 1
20.0%

0,5 Mbps
Real number (ℝ)

MISSING  SKEWED 

Distinct35
Distinct (%)5.2%
Missing2417
Missing (%)78.3%
Infinite0
Infinite (%)0.0%
Mean49.967115
Minimum0
Maximum30362
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size24.2 KiB
2023-01-03T11:55:01.187430image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q33
95-th percentile11
Maximum30362
Range30362
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1173.9069
Coefficient of variation (CV)23.49359
Kurtosis668.49266
Mean49.967115
Median Absolute Deviation (MAD)0
Skewness25.850453
Sum33428
Variance1378057.5
MonotonicityNot monotonic
2023-01-03T11:55:01.268495image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
1 383
 
12.4%
2 115
 
3.7%
3 51
 
1.7%
4 25
 
0.8%
5 19
 
0.6%
8 11
 
0.4%
7 10
 
0.3%
14 7
 
0.2%
6 7
 
0.2%
9 6
 
0.2%
Other values (25) 35
 
1.1%
(Missing) 2417
78.3%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 383
12.4%
2 115
 
3.7%
3 51
 
1.7%
4 25
 
0.8%
5 19
 
0.6%
6 7
 
0.2%
7 10
 
0.3%
8 11
 
0.4%
9 6
 
0.2%
ValueCountFrequency (%)
30362 1
< 0.1%
461 1
< 0.1%
232 1
< 0.1%
206 1
< 0.1%
134 1
< 0.1%
89 1
< 0.1%
74 1
< 0.1%
71 1
< 0.1%
36 1
< 0.1%
33 2
0.1%

0,512 Mbps
Real number (ℝ)

Distinct19
Distinct (%)3.6%
Missing2562
Missing (%)83.0%
Infinite0
Infinite (%)0.0%
Mean2.1736641
Minimum1
Maximum61
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.2 KiB
2023-01-03T11:55:01.341555image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile5.85
Maximum61
Range60
Interquartile range (IQR)0

Descriptive statistics

Standard deviation4.8221122
Coefficient of variation (CV)2.2184256
Kurtosis71.748645
Mean2.1736641
Median Absolute Deviation (MAD)0
Skewness7.8040315
Sum1139
Variance23.252766
MonotonicityNot monotonic
2023-01-03T11:55:01.404881image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
1 403
 
13.1%
2 44
 
1.4%
3 22
 
0.7%
5 17
 
0.6%
4 11
 
0.4%
6 6
 
0.2%
9 4
 
0.1%
8 3
 
0.1%
10 3
 
0.1%
35 2
 
0.1%
Other values (9) 9
 
0.3%
(Missing) 2562
83.0%
ValueCountFrequency (%)
1 403
13.1%
2 44
 
1.4%
3 22
 
0.7%
4 11
 
0.4%
5 17
 
0.6%
6 6
 
0.2%
7 1
 
< 0.1%
8 3
 
0.1%
9 4
 
0.1%
10 3
 
0.1%
ValueCountFrequency (%)
61 1
 
< 0.1%
46 1
 
< 0.1%
39 1
 
< 0.1%
35 2
0.1%
30 1
 
< 0.1%
24 1
 
< 0.1%
23 1
 
< 0.1%
14 1
 
< 0.1%
13 1
 
< 0.1%
10 3
0.1%

0,625 Mbps
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing3085
Missing (%)> 99.9%
Memory size24.2 KiB
111.0

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters5
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row111.0

Common Values

ValueCountFrequency (%)
111.0 1
 
< 0.1%
(Missing) 3085
> 99.9%

Length

2023-01-03T11:55:01.476511image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-03T11:55:01.542091image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
111.0 1
100.0%

Most occurring characters

ValueCountFrequency (%)
1 3
60.0%
. 1
 
20.0%
0 1
 
20.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4
80.0%
Other Punctuation 1
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 3
75.0%
0 1
 
25.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 3
60.0%
. 1
 
20.0%
0 1
 
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 3
60.0%
. 1
 
20.0%
0 1
 
20.0%

0,75 Mbps
Real number (ℝ)

Distinct91
Distinct (%)28.8%
Missing2770
Missing (%)89.8%
Infinite0
Infinite (%)0.0%
Mean45.35443
Minimum1
Maximum1407
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.2 KiB
2023-01-03T11:55:01.605402image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12.75
median11
Q331
95-th percentile166.25
Maximum1407
Range1406
Interquartile range (IQR)28.25

Descriptive statistics

Standard deviation129.95015
Coefficient of variation (CV)2.8652141
Kurtosis52.994465
Mean45.35443
Median Absolute Deviation (MAD)9
Skewness6.5776502
Sum14332
Variance16887.042
MonotonicityNot monotonic
2023-01-03T11:55:01.688909image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 51
 
1.7%
2 28
 
0.9%
3 23
 
0.7%
5 15
 
0.5%
4 10
 
0.3%
8 10
 
0.3%
7 9
 
0.3%
16 9
 
0.3%
15 9
 
0.3%
12 8
 
0.3%
Other values (81) 144
 
4.7%
(Missing) 2770
89.8%
ValueCountFrequency (%)
1 51
1.7%
2 28
0.9%
3 23
0.7%
4 10
 
0.3%
5 15
 
0.5%
6 4
 
0.1%
7 9
 
0.3%
8 10
 
0.3%
9 5
 
0.2%
10 2
 
0.1%
ValueCountFrequency (%)
1407 1
< 0.1%
929 1
< 0.1%
908 1
< 0.1%
669 1
< 0.1%
597 1
< 0.1%
476 1
< 0.1%
467 1
< 0.1%
343 1
< 0.1%
331 1
< 0.1%
313 1
< 0.1%

1 Mbps
Real number (ℝ)

Distinct223
Distinct (%)23.5%
Missing2138
Missing (%)69.3%
Infinite0
Infinite (%)0.0%
Mean95.355485
Minimum1
Maximum6307
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.2 KiB
2023-01-03T11:55:01.783325image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median14
Q358.25
95-th percentile394.65
Maximum6307
Range6306
Interquartile range (IQR)55.25

Descriptive statistics

Standard deviation357.62947
Coefficient of variation (CV)3.7504866
Kurtosis148.4676
Mean95.355485
Median Absolute Deviation (MAD)13
Skewness10.852238
Sum90397
Variance127898.84
MonotonicityNot monotonic
2023-01-03T11:55:01.872615image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 141
 
4.6%
2 66
 
2.1%
3 48
 
1.6%
5 40
 
1.3%
4 36
 
1.2%
7 25
 
0.8%
6 21
 
0.7%
15 19
 
0.6%
16 18
 
0.6%
9 18
 
0.6%
Other values (213) 516
 
16.7%
(Missing) 2138
69.3%
ValueCountFrequency (%)
1 141
4.6%
2 66
2.1%
3 48
 
1.6%
4 36
 
1.2%
5 40
 
1.3%
6 21
 
0.7%
7 25
 
0.8%
8 17
 
0.6%
9 18
 
0.6%
10 14
 
0.5%
ValueCountFrequency (%)
6307 1
< 0.1%
4425 1
< 0.1%
4168 1
< 0.1%
3666 1
< 0.1%
2122 1
< 0.1%
1997 1
< 0.1%
1755 1
< 0.1%
1472 1
< 0.1%
1347 1
< 0.1%
1314 1
< 0.1%

1,25 Mbps
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing3085
Missing (%)> 99.9%
Memory size24.2 KiB
25.0

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters4
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row25.0

Common Values

ValueCountFrequency (%)
25.0 1
 
< 0.1%
(Missing) 3085
> 99.9%

Length

2023-01-03T11:55:01.952760image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-03T11:55:02.017849image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
25.0 1
100.0%

Most occurring characters

ValueCountFrequency (%)
2 1
25.0%
5 1
25.0%
. 1
25.0%
0 1
25.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3
75.0%
Other Punctuation 1
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 1
33.3%
5 1
33.3%
0 1
33.3%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 1
25.0%
5 1
25.0%
. 1
25.0%
0 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 1
25.0%
5 1
25.0%
. 1
25.0%
0 1
25.0%

1,5 Mbps
Real number (ℝ)

Distinct15
Distinct (%)93.8%
Missing3070
Missing (%)99.5%
Infinite0
Infinite (%)0.0%
Mean137.375
Minimum2
Maximum1170
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.2 KiB
2023-01-03T11:55:02.067744image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile5.75
Q121.75
median44.5
Q370.5
95-th percentile554.25
Maximum1170
Range1168
Interquartile range (IQR)48.75

Descriptive statistics

Standard deviation288.76701
Coefficient of variation (CV)2.1020346
Kurtosis12.63695
Mean137.375
Median Absolute Deviation (MAD)24
Skewness3.4670813
Sum2198
Variance83386.383
MonotonicityNot monotonic
2023-01-03T11:55:02.134665image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
47 2
 
0.1%
2 1
 
< 0.1%
349 1
 
< 0.1%
7 1
 
< 0.1%
23 1
 
< 0.1%
40 1
 
< 0.1%
14 1
 
< 0.1%
42 1
 
< 0.1%
66 1
 
< 0.1%
1170 1
 
< 0.1%
Other values (5) 5
 
0.2%
(Missing) 3070
99.5%
ValueCountFrequency (%)
2 1
< 0.1%
7 1
< 0.1%
14 1
< 0.1%
18 1
< 0.1%
23 1
< 0.1%
39 1
< 0.1%
40 1
< 0.1%
42 1
< 0.1%
47 2
0.1%
58 1
< 0.1%
ValueCountFrequency (%)
1170 1
< 0.1%
349 1
< 0.1%
192 1
< 0.1%
84 1
< 0.1%
66 1
< 0.1%
58 1
< 0.1%
47 2
0.1%
42 1
< 0.1%
40 1
< 0.1%
39 1
< 0.1%

2 Mbps
Real number (ℝ)

Distinct238
Distinct (%)31.0%
Missing2318
Missing (%)75.1%
Infinite0
Infinite (%)0.0%
Mean126.00651
Minimum1
Maximum3530
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.2 KiB
2023-01-03T11:55:02.217099image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q15
median25
Q393.25
95-th percentile640.9
Maximum3530
Range3529
Interquartile range (IQR)88.25

Descriptive statistics

Standard deviation320.5279
Coefficient of variation (CV)2.5437408
Kurtosis42.703892
Mean126.00651
Median Absolute Deviation (MAD)23
Skewness5.6483404
Sum96773
Variance102738.13
MonotonicityNot monotonic
2023-01-03T11:55:02.302552image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 85
 
2.8%
2 49
 
1.6%
3 29
 
0.9%
4 26
 
0.8%
5 24
 
0.8%
6 18
 
0.6%
30 14
 
0.5%
10 13
 
0.4%
7 12
 
0.4%
9 12
 
0.4%
Other values (228) 486
 
15.7%
(Missing) 2318
75.1%
ValueCountFrequency (%)
1 85
2.8%
2 49
1.6%
3 29
 
0.9%
4 26
 
0.8%
5 24
 
0.8%
6 18
 
0.6%
7 12
 
0.4%
8 10
 
0.3%
9 12
 
0.4%
10 13
 
0.4%
ValueCountFrequency (%)
3530 1
< 0.1%
3515 1
< 0.1%
2251 1
< 0.1%
2065 1
< 0.1%
1993 1
< 0.1%
1931 1
< 0.1%
1916 1
< 0.1%
1625 1
< 0.1%
1587 1
< 0.1%
1552 1
< 0.1%

2,2 Mbps
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing3085
Missing (%)> 99.9%
Memory size24.2 KiB
26.0

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters4
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row26.0

Common Values

ValueCountFrequency (%)
26.0 1
 
< 0.1%
(Missing) 3085
> 99.9%

Length

2023-01-03T11:55:02.383795image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-03T11:55:02.460182image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
26.0 1
100.0%

Most occurring characters

ValueCountFrequency (%)
2 1
25.0%
6 1
25.0%
. 1
25.0%
0 1
25.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3
75.0%
Other Punctuation 1
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 1
33.3%
6 1
33.3%
0 1
33.3%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 1
25.0%
6 1
25.0%
. 1
25.0%
0 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 1
25.0%
6 1
25.0%
. 1
25.0%
0 1
25.0%

2,5 Mbps
Categorical

MISSING  UNIFORM 

Distinct3
Distinct (%)100.0%
Missing3083
Missing (%)99.9%
Memory size24.2 KiB
25.0
114.0
40.0

Length

Max length5
Median length4
Mean length4.3333333
Min length4

Characters and Unicode

Total characters13
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)100.0%

Sample

1st row25.0
2nd row114.0
3rd row40.0

Common Values

ValueCountFrequency (%)
25.0 1
 
< 0.1%
114.0 1
 
< 0.1%
40.0 1
 
< 0.1%
(Missing) 3083
99.9%

Length

2023-01-03T11:55:02.520667image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-03T11:55:02.598852image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
25.0 1
33.3%
114.0 1
33.3%
40.0 1
33.3%

Most occurring characters

ValueCountFrequency (%)
0 4
30.8%
. 3
23.1%
1 2
15.4%
4 2
15.4%
2 1
 
7.7%
5 1
 
7.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10
76.9%
Other Punctuation 3
 
23.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4
40.0%
1 2
20.0%
4 2
20.0%
2 1
 
10.0%
5 1
 
10.0%
Other Punctuation
ValueCountFrequency (%)
. 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 13
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4
30.8%
. 3
23.1%
1 2
15.4%
4 2
15.4%
2 1
 
7.7%
5 1
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4
30.8%
. 3
23.1%
1 2
15.4%
4 2
15.4%
2 1
 
7.7%
5 1
 
7.7%

3 Mbps
Real number (ℝ)

Distinct414
Distinct (%)30.1%
Missing1712
Missing (%)55.5%
Infinite0
Infinite (%)0.0%
Mean212.52256
Minimum1
Maximum13003
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.2 KiB
2023-01-03T11:55:02.675042image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q16.25
median34
Q3149.5
95-th percentile896.5
Maximum13003
Range13002
Interquartile range (IQR)143.25

Descriptive statistics

Standard deviation724.80361
Coefficient of variation (CV)3.4104784
Kurtosis157.80629
Mean212.52256
Median Absolute Deviation (MAD)32
Skewness10.916421
Sum292006
Variance525340.27
MonotonicityNot monotonic
2023-01-03T11:55:02.775658image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 114
 
3.7%
2 61
 
2.0%
3 53
 
1.7%
5 47
 
1.5%
4 35
 
1.1%
6 34
 
1.1%
8 28
 
0.9%
10 22
 
0.7%
9 22
 
0.7%
15 19
 
0.6%
Other values (404) 939
30.4%
(Missing) 1712
55.5%
ValueCountFrequency (%)
1 114
3.7%
2 61
2.0%
3 53
1.7%
4 35
 
1.1%
5 47
1.5%
6 34
 
1.1%
7 17
 
0.6%
8 28
 
0.9%
9 22
 
0.7%
10 22
 
0.7%
ValueCountFrequency (%)
13003 1
< 0.1%
12504 1
< 0.1%
8801 1
< 0.1%
6834 1
< 0.1%
6628 1
< 0.1%
5393 1
< 0.1%
4863 1
< 0.1%
3895 1
< 0.1%
3463 1
< 0.1%
3387 1
< 0.1%

3,3 Mbps
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing3085
Missing (%)> 99.9%
Memory size24.2 KiB
6.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row6.0

Common Values

ValueCountFrequency (%)
6.0 1
 
< 0.1%
(Missing) 3085
> 99.9%

Length

2023-01-03T11:55:02.859605image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-03T11:55:02.929940image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
6.0 1
100.0%

Most occurring characters

ValueCountFrequency (%)
6 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2
66.7%
Other Punctuation 1
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
6 1
50.0%
0 1
50.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
6 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6 1
33.3%
. 1
33.3%
0 1
33.3%

3,5 Mbps
Real number (ℝ)

Distinct222
Distinct (%)58.0%
Missing2703
Missing (%)87.6%
Infinite0
Infinite (%)0.0%
Mean381.26371
Minimum1
Maximum13670
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.2 KiB
2023-01-03T11:55:02.997471image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q111
median62
Q3258
95-th percentile1666.5
Maximum13670
Range13669
Interquartile range (IQR)247

Descriptive statistics

Standard deviation1077.5777
Coefficient of variation (CV)2.8263318
Kurtosis69.289547
Mean381.26371
Median Absolute Deviation (MAD)59
Skewness7.0660428
Sum146024
Variance1161173.8
MonotonicityNot monotonic
2023-01-03T11:55:03.085962image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 25
 
0.8%
2 18
 
0.6%
4 12
 
0.4%
8 9
 
0.3%
5 8
 
0.3%
3 8
 
0.3%
7 7
 
0.2%
6 6
 
0.2%
23 5
 
0.2%
13 5
 
0.2%
Other values (212) 280
 
9.1%
(Missing) 2703
87.6%
ValueCountFrequency (%)
1 25
0.8%
2 18
0.6%
3 8
 
0.3%
4 12
0.4%
5 8
 
0.3%
6 6
 
0.2%
7 7
 
0.2%
8 9
 
0.3%
9 1
 
< 0.1%
10 1
 
< 0.1%
ValueCountFrequency (%)
13670 1
< 0.1%
7805 1
< 0.1%
5528 1
< 0.1%
4898 1
< 0.1%
4562 1
< 0.1%
4399 1
< 0.1%
4398 1
< 0.1%
4297 1
< 0.1%
3237 1
< 0.1%
3123 1
< 0.1%

4 Mbps
Real number (ℝ)

Distinct202
Distinct (%)37.3%
Missing2544
Missing (%)82.4%
Infinite0
Infinite (%)0.0%
Mean137.64391
Minimum1
Maximum4429
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.2 KiB
2023-01-03T11:55:03.185865image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q16
median25
Q399
95-th percentile604.05
Maximum4429
Range4428
Interquartile range (IQR)93

Descriptive statistics

Standard deviation367.9211
Coefficient of variation (CV)2.6729922
Kurtosis48.992748
Mean137.64391
Median Absolute Deviation (MAD)23
Skewness6.0356101
Sum74603
Variance135365.94
MonotonicityNot monotonic
2023-01-03T11:55:03.275397image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 47
 
1.5%
2 29
 
0.9%
3 28
 
0.9%
7 18
 
0.6%
5 15
 
0.5%
4 13
 
0.4%
6 12
 
0.4%
10 11
 
0.4%
12 10
 
0.3%
15 9
 
0.3%
Other values (192) 350
 
11.3%
(Missing) 2544
82.4%
ValueCountFrequency (%)
1 47
1.5%
2 29
0.9%
3 28
0.9%
4 13
 
0.4%
5 15
 
0.5%
6 12
 
0.4%
7 18
 
0.6%
8 9
 
0.3%
9 8
 
0.3%
10 11
 
0.4%
ValueCountFrequency (%)
4429 1
< 0.1%
3005 1
< 0.1%
2406 1
< 0.1%
2257 1
< 0.1%
2080 1
< 0.1%
1886 1
< 0.1%
1780 1
< 0.1%
1640 1
< 0.1%
1624 1
< 0.1%
1564 1
< 0.1%

4,5 Mbps
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing3085
Missing (%)> 99.9%
Memory size24.2 KiB
65.0

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters4
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row65.0

Common Values

ValueCountFrequency (%)
65.0 1
 
< 0.1%
(Missing) 3085
> 99.9%

Length

2023-01-03T11:55:03.356769image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-03T11:55:03.422608image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
65.0 1
100.0%

Most occurring characters

ValueCountFrequency (%)
6 1
25.0%
5 1
25.0%
. 1
25.0%
0 1
25.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3
75.0%
Other Punctuation 1
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
6 1
33.3%
5 1
33.3%
0 1
33.3%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
6 1
25.0%
5 1
25.0%
. 1
25.0%
0 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6 1
25.0%
5 1
25.0%
. 1
25.0%
0 1
25.0%

5 Mbps
Real number (ℝ)

Distinct264
Distinct (%)26.7%
Missing2096
Missing (%)67.9%
Infinite0
Infinite (%)0.0%
Mean118.48586
Minimum0
Maximum6618
Zeros5
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size24.2 KiB
2023-01-03T11:55:03.491746image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median14
Q375
95-th percentile555.65
Maximum6618
Range6618
Interquartile range (IQR)72

Descriptive statistics

Standard deviation366.36
Coefficient of variation (CV)3.0920146
Kurtosis120.85069
Mean118.48586
Median Absolute Deviation (MAD)13
Skewness8.9504986
Sum117301
Variance134219.65
MonotonicityNot monotonic
2023-01-03T11:55:03.576014image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 118
 
3.8%
2 76
 
2.5%
3 58
 
1.9%
5 56
 
1.8%
4 42
 
1.4%
6 33
 
1.1%
10 21
 
0.7%
7 17
 
0.6%
8 15
 
0.5%
9 14
 
0.5%
Other values (254) 540
 
17.5%
(Missing) 2096
67.9%
ValueCountFrequency (%)
0 5
 
0.2%
1 118
3.8%
2 76
2.5%
3 58
1.9%
4 42
 
1.4%
5 56
1.8%
6 33
 
1.1%
7 17
 
0.6%
8 15
 
0.5%
9 14
 
0.5%
ValueCountFrequency (%)
6618 1
< 0.1%
3596 1
< 0.1%
3506 1
< 0.1%
2510 1
< 0.1%
2054 1
< 0.1%
2040 1
< 0.1%
1966 1
< 0.1%
1912 1
< 0.1%
1707 1
< 0.1%
1704 1
< 0.1%

6 Mbps
Real number (ℝ)

Distinct518
Distinct (%)35.7%
Missing1635
Missing (%)53.0%
Infinite0
Infinite (%)0.0%
Mean368.06823
Minimum1
Maximum21326
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.2 KiB
2023-01-03T11:55:03.670646image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q113
median63
Q3259.5
95-th percentile1318
Maximum21326
Range21325
Interquartile range (IQR)246.5

Descriptive statistics

Standard deviation1337.3066
Coefficient of variation (CV)3.6333116
Kurtosis125.08982
Mean368.06823
Median Absolute Deviation (MAD)59
Skewness10.160065
Sum534067
Variance1788388.8
MonotonicityNot monotonic
2023-01-03T11:55:03.910635image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 79
 
2.6%
2 58
 
1.9%
3 43
 
1.4%
4 32
 
1.0%
5 24
 
0.8%
9 20
 
0.6%
8 20
 
0.6%
10 19
 
0.6%
20 18
 
0.6%
28 18
 
0.6%
Other values (508) 1120
36.3%
(Missing) 1635
53.0%
ValueCountFrequency (%)
1 79
2.6%
2 58
1.9%
3 43
1.4%
4 32
1.0%
5 24
 
0.8%
6 17
 
0.6%
7 14
 
0.5%
8 20
 
0.6%
9 20
 
0.6%
10 19
 
0.6%
ValueCountFrequency (%)
21326 1
< 0.1%
19985 1
< 0.1%
16763 1
< 0.1%
16041 1
< 0.1%
14409 1
< 0.1%
13863 1
< 0.1%
12194 1
< 0.1%
12150 1
< 0.1%
6189 1
< 0.1%
6007 1
< 0.1%

6,4 Mbps
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing3085
Missing (%)> 99.9%
Memory size24.2 KiB
13.0

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters4
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row13.0

Common Values

ValueCountFrequency (%)
13.0 1
 
< 0.1%
(Missing) 3085
> 99.9%

Length

2023-01-03T11:55:03.984947image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-03T11:55:04.047822image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
13.0 1
100.0%

Most occurring characters

ValueCountFrequency (%)
1 1
25.0%
3 1
25.0%
. 1
25.0%
0 1
25.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3
75.0%
Other Punctuation 1
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1
33.3%
3 1
33.3%
0 1
33.3%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1
25.0%
3 1
25.0%
. 1
25.0%
0 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1
25.0%
3 1
25.0%
. 1
25.0%
0 1
25.0%

7 Mbps
Real number (ℝ)

Distinct109
Distinct (%)41.4%
Missing2823
Missing (%)91.5%
Infinite0
Infinite (%)0.0%
Mean104.39544
Minimum1
Maximum4211
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.2 KiB
2023-01-03T11:55:04.113681image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q15
median16
Q355.5
95-th percentile403.4
Maximum4211
Range4210
Interquartile range (IQR)50.5

Descriptive statistics

Standard deviation350.85748
Coefficient of variation (CV)3.3608507
Kurtosis76.087621
Mean104.39544
Median Absolute Deviation (MAD)14
Skewness7.654499
Sum27456
Variance123100.97
MonotonicityNot monotonic
2023-01-03T11:55:04.197412image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 28
 
0.9%
2 20
 
0.6%
8 9
 
0.3%
11 8
 
0.3%
3 8
 
0.3%
7 7
 
0.2%
9 7
 
0.2%
6 7
 
0.2%
10 7
 
0.2%
4 7
 
0.2%
Other values (99) 155
 
5.0%
(Missing) 2823
91.5%
ValueCountFrequency (%)
1 28
0.9%
2 20
0.6%
3 8
 
0.3%
4 7
 
0.2%
5 7
 
0.2%
6 7
 
0.2%
7 7
 
0.2%
8 9
 
0.3%
9 7
 
0.2%
10 7
 
0.2%
ValueCountFrequency (%)
4211 1
< 0.1%
1708 1
< 0.1%
1659 1
< 0.1%
1518 1
< 0.1%
1352 1
< 0.1%
1116 1
< 0.1%
1094 1
< 0.1%
1039 1
< 0.1%
970 1
< 0.1%
788 1
< 0.1%

7,5 Mbps
Categorical

MISSING  UNIFORM 

Distinct4
Distinct (%)100.0%
Missing3082
Missing (%)99.9%
Memory size24.2 KiB
18.0
213.0
10.0
48.0

Length

Max length5
Median length4
Mean length4.25
Min length4

Characters and Unicode

Total characters17
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)100.0%

Sample

1st row18.0
2nd row213.0
3rd row10.0
4th row48.0

Common Values

ValueCountFrequency (%)
18.0 1
 
< 0.1%
213.0 1
 
< 0.1%
10.0 1
 
< 0.1%
48.0 1
 
< 0.1%
(Missing) 3082
99.9%

Length

2023-01-03T11:55:04.274601image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-03T11:55:04.355489image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
18.0 1
25.0%
213.0 1
25.0%
10.0 1
25.0%
48.0 1
25.0%

Most occurring characters

ValueCountFrequency (%)
0 5
29.4%
. 4
23.5%
1 3
17.6%
8 2
 
11.8%
2 1
 
5.9%
3 1
 
5.9%
4 1
 
5.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 13
76.5%
Other Punctuation 4
 
23.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5
38.5%
1 3
23.1%
8 2
 
15.4%
2 1
 
7.7%
3 1
 
7.7%
4 1
 
7.7%
Other Punctuation
ValueCountFrequency (%)
. 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 17
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 5
29.4%
. 4
23.5%
1 3
17.6%
8 2
 
11.8%
2 1
 
5.9%
3 1
 
5.9%
4 1
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 17
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 5
29.4%
. 4
23.5%
1 3
17.6%
8 2
 
11.8%
2 1
 
5.9%
3 1
 
5.9%
4 1
 
5.9%

8 Mbps
Real number (ℝ)

Distinct295
Distinct (%)43.5%
Missing2408
Missing (%)78.0%
Infinite0
Infinite (%)0.0%
Mean289.67257
Minimum1
Maximum18196
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.2 KiB
2023-01-03T11:55:04.436479image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q111
median48
Q3212.25
95-th percentile1269.85
Maximum18196
Range18195
Interquartile range (IQR)201.25

Descriptive statistics

Standard deviation933.36175
Coefficient of variation (CV)3.2221268
Kurtosis208.13879
Mean289.67257
Median Absolute Deviation (MAD)45
Skewness12.109801
Sum196398
Variance871164.16
MonotonicityNot monotonic
2023-01-03T11:55:04.526168image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 45
 
1.5%
2 28
 
0.9%
3 19
 
0.6%
5 16
 
0.5%
8 14
 
0.5%
21 12
 
0.4%
6 11
 
0.4%
10 10
 
0.3%
7 10
 
0.3%
20 10
 
0.3%
Other values (285) 503
 
16.3%
(Missing) 2408
78.0%
ValueCountFrequency (%)
1 45
1.5%
2 28
0.9%
3 19
0.6%
4 7
 
0.2%
5 16
 
0.5%
6 11
 
0.4%
7 10
 
0.3%
8 14
 
0.5%
9 8
 
0.3%
10 10
 
0.3%
ValueCountFrequency (%)
18196 1
< 0.1%
7503 1
< 0.1%
5956 1
< 0.1%
3927 1
< 0.1%
3841 1
< 0.1%
3734 1
< 0.1%
3545 1
< 0.1%
3185 1
< 0.1%
3142 1
< 0.1%
2881 1
< 0.1%

9 Mbps
Real number (ℝ)

Distinct28
Distinct (%)53.8%
Missing3034
Missing (%)98.3%
Infinite0
Infinite (%)0.0%
Mean143.71154
Minimum1
Maximum1884
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.2 KiB
2023-01-03T11:55:04.615430image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median11
Q353.25
95-th percentile969.6
Maximum1884
Range1883
Interquartile range (IQR)50.25

Descriptive statistics

Standard deviation387.23977
Coefficient of variation (CV)2.6945628
Kurtosis11.586821
Mean143.71154
Median Absolute Deviation (MAD)10
Skewness3.4448974
Sum7473
Variance149954.64
MonotonicityNot monotonic
2023-01-03T11:55:04.687373image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
1 9
 
0.3%
3 7
 
0.2%
2 3
 
0.1%
14 3
 
0.1%
32 2
 
0.1%
13 2
 
0.1%
4 2
 
0.1%
6 2
 
0.1%
9 2
 
0.1%
69 2
 
0.1%
Other values (18) 18
 
0.6%
(Missing) 3034
98.3%
ValueCountFrequency (%)
1 9
0.3%
2 3
 
0.1%
3 7
0.2%
4 2
 
0.1%
6 2
 
0.1%
7 1
 
< 0.1%
9 2
 
0.1%
13 2
 
0.1%
14 3
 
0.1%
18 1
 
< 0.1%
ValueCountFrequency (%)
1884 1
< 0.1%
1544 1
< 0.1%
1315 1
< 0.1%
687 1
< 0.1%
638 1
< 0.1%
248 1
< 0.1%
236 1
< 0.1%
171 1
< 0.1%
77 1
< 0.1%
69 2
0.1%

10 Mbps
Real number (ℝ)

Distinct680
Distinct (%)41.0%
Missing1426
Missing (%)46.2%
Infinite0
Infinite (%)0.0%
Mean588.86687
Minimum1
Maximum89427
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.2 KiB
2023-01-03T11:55:04.778759image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q19
median72.5
Q3427
95-th percentile1844.45
Maximum89427
Range89426
Interquartile range (IQR)418

Descriptive statistics

Standard deviation3366.945
Coefficient of variation (CV)5.7176676
Kurtosis396.50249
Mean588.86687
Median Absolute Deviation (MAD)71
Skewness18.303343
Sum977519
Variance11336319
MonotonicityNot monotonic
2023-01-03T11:55:04.874649image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 174
 
5.6%
2 59
 
1.9%
3 46
 
1.5%
4 33
 
1.1%
5 28
 
0.9%
6 24
 
0.8%
10 23
 
0.7%
9 22
 
0.7%
7 20
 
0.6%
8 19
 
0.6%
Other values (670) 1212
39.3%
(Missing) 1426
46.2%
ValueCountFrequency (%)
1 174
5.6%
2 59
 
1.9%
3 46
 
1.5%
4 33
 
1.1%
5 28
 
0.9%
6 24
 
0.8%
7 20
 
0.6%
8 19
 
0.6%
9 22
 
0.7%
10 23
 
0.7%
ValueCountFrequency (%)
89427 1
< 0.1%
59184 1
< 0.1%
49612 1
< 0.1%
43915 1
< 0.1%
32385 1
< 0.1%
25404 1
< 0.1%
15696 1
< 0.1%
11722 1
< 0.1%
10383 1
< 0.1%
7767 1
< 0.1%

11 Mbps
Real number (ℝ)

Distinct9
Distinct (%)33.3%
Missing3059
Missing (%)99.1%
Infinite0
Infinite (%)0.0%
Mean5.4814815
Minimum1
Maximum25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.2 KiB
2023-01-03T11:55:04.951079image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q35
95-th percentile24.1
Maximum25
Range24
Interquartile range (IQR)4

Descriptive statistics

Standard deviation7.5771436
Coefficient of variation (CV)1.3823167
Kurtosis2.4179937
Mean5.4814815
Median Absolute Deviation (MAD)1
Skewness1.9274358
Sum148
Variance57.413105
MonotonicityNot monotonic
2023-01-03T11:55:05.017583image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1 10
 
0.3%
2 6
 
0.2%
3 2
 
0.1%
5 2
 
0.1%
8 2
 
0.1%
25 2
 
0.1%
22 1
 
< 0.1%
4 1
 
< 0.1%
18 1
 
< 0.1%
(Missing) 3059
99.1%
ValueCountFrequency (%)
1 10
0.3%
2 6
0.2%
3 2
 
0.1%
4 1
 
< 0.1%
5 2
 
0.1%
8 2
 
0.1%
18 1
 
< 0.1%
22 1
 
< 0.1%
25 2
 
0.1%
ValueCountFrequency (%)
25 2
 
0.1%
22 1
 
< 0.1%
18 1
 
< 0.1%
8 2
 
0.1%
5 2
 
0.1%
4 1
 
< 0.1%
3 2
 
0.1%
2 6
0.2%
1 10
0.3%

12 Mbps
Real number (ℝ)

Distinct226
Distinct (%)44.1%
Missing2574
Missing (%)83.4%
Infinite0
Infinite (%)0.0%
Mean452.53711
Minimum1
Maximum38432
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.2 KiB
2023-01-03T11:55:05.100526image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q17
median41
Q3157.25
95-th percentile1540.4
Maximum38432
Range38431
Interquartile range (IQR)150.25

Descriptive statistics

Standard deviation2295.1577
Coefficient of variation (CV)5.0717557
Kurtosis167.42844
Mean452.53711
Median Absolute Deviation (MAD)38
Skewness11.671014
Sum231699
Variance5267748.7
MonotonicityNot monotonic
2023-01-03T11:55:05.193897image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 47
 
1.5%
2 21
 
0.7%
3 17
 
0.6%
5 12
 
0.4%
4 12
 
0.4%
8 12
 
0.4%
7 12
 
0.4%
11 11
 
0.4%
14 8
 
0.3%
6 8
 
0.3%
Other values (216) 352
 
11.4%
(Missing) 2574
83.4%
ValueCountFrequency (%)
1 47
1.5%
2 21
0.7%
3 17
 
0.6%
4 12
 
0.4%
5 12
 
0.4%
6 8
 
0.3%
7 12
 
0.4%
8 12
 
0.4%
9 6
 
0.2%
10 7
 
0.2%
ValueCountFrequency (%)
38432 1
< 0.1%
22758 1
< 0.1%
13746 1
< 0.1%
10207 1
< 0.1%
9389 1
< 0.1%
8741 1
< 0.1%
8135 1
< 0.1%
7860 1
< 0.1%
6959 1
< 0.1%
4423 1
< 0.1%

13 Mbps
Real number (ℝ)

Distinct9
Distinct (%)60.0%
Missing3071
Missing (%)99.5%
Infinite0
Infinite (%)0.0%
Mean19.866667
Minimum1
Maximum112
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.2 KiB
2023-01-03T11:55:05.275461image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q311.5
95-th percentile96.6
Maximum112
Range111
Interquartile range (IQR)9.5

Descriptive statistics

Standard deviation34.69637
Coefficient of variation (CV)1.7464616
Kurtosis3.6098096
Mean19.866667
Median Absolute Deviation (MAD)3
Skewness2.1427988
Sum298
Variance1203.8381
MonotonicityNot monotonic
2023-01-03T11:55:05.338550image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1 3
 
0.1%
4 3
 
0.1%
2 2
 
0.1%
7 2
 
0.1%
112 1
 
< 0.1%
6 1
 
< 0.1%
16 1
 
< 0.1%
41 1
 
< 0.1%
90 1
 
< 0.1%
(Missing) 3071
99.5%
ValueCountFrequency (%)
1 3
0.1%
2 2
0.1%
4 3
0.1%
6 1
 
< 0.1%
7 2
0.1%
16 1
 
< 0.1%
41 1
 
< 0.1%
90 1
 
< 0.1%
112 1
 
< 0.1%
ValueCountFrequency (%)
112 1
 
< 0.1%
90 1
 
< 0.1%
41 1
 
< 0.1%
16 1
 
< 0.1%
7 2
0.1%
6 1
 
< 0.1%
4 3
0.1%
2 2
0.1%
1 3
0.1%

14 Mbps
Real number (ℝ)

Distinct6
Distinct (%)30.0%
Missing3066
Missing (%)99.4%
Infinite0
Infinite (%)0.0%
Mean2.3
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.2 KiB
2023-01-03T11:55:05.408443image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q33
95-th percentile6.15
Maximum9
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.1299864
Coefficient of variation (CV)0.92608105
Kurtosis4.4903387
Mean2.3
Median Absolute Deviation (MAD)0
Skewness2.105246
Sum46
Variance4.5368421
MonotonicityNot monotonic
2023-01-03T11:55:05.476498image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 11
 
0.4%
3 3
 
0.1%
2 3
 
0.1%
6 1
 
< 0.1%
5 1
 
< 0.1%
9 1
 
< 0.1%
(Missing) 3066
99.4%
ValueCountFrequency (%)
1 11
0.4%
2 3
 
0.1%
3 3
 
0.1%
5 1
 
< 0.1%
6 1
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
9 1
 
< 0.1%
6 1
 
< 0.1%
5 1
 
< 0.1%
3 3
 
0.1%
2 3
 
0.1%
1 11
0.4%

15 Mbps
Real number (ℝ)

Distinct372
Distinct (%)36.9%
Missing2078
Missing (%)67.3%
Infinite0
Infinite (%)0.0%
Mean272.00694
Minimum0
Maximum31903
Zeros3
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size24.2 KiB
2023-01-03T11:55:05.567728image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q110
median51
Q3175.75
95-th percentile1137.85
Maximum31903
Range31903
Interquartile range (IQR)165.75

Descriptive statistics

Standard deviation1201.0393
Coefficient of variation (CV)4.4154728
Kurtosis483.52981
Mean272.00694
Median Absolute Deviation (MAD)47
Skewness19.321275
Sum274183
Variance1442495.3
MonotonicityNot monotonic
2023-01-03T11:55:05.657476image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 82
 
2.7%
2 34
 
1.1%
3 26
 
0.8%
4 23
 
0.7%
10 21
 
0.7%
8 20
 
0.6%
5 18
 
0.6%
6 16
 
0.5%
7 15
 
0.5%
15 15
 
0.5%
Other values (362) 738
 
23.9%
(Missing) 2078
67.3%
ValueCountFrequency (%)
0 3
 
0.1%
1 82
2.7%
2 34
1.1%
3 26
 
0.8%
4 23
 
0.7%
5 18
 
0.6%
6 16
 
0.5%
7 15
 
0.5%
8 20
 
0.6%
9 10
 
0.3%
ValueCountFrequency (%)
31903 1
< 0.1%
8713 1
< 0.1%
7516 1
< 0.1%
6406 1
< 0.1%
6136 1
< 0.1%
4578 1
< 0.1%
4508 1
< 0.1%
4489 1
< 0.1%
4249 1
< 0.1%
3318 1
< 0.1%

16 Mbps
Real number (ℝ)

Distinct14
Distinct (%)77.8%
Missing3068
Missing (%)99.4%
Infinite0
Infinite (%)0.0%
Mean89.5
Minimum1
Maximum1093
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.2 KiB
2023-01-03T11:55:05.738638image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11.25
median11
Q351.5
95-th percentile322.05
Maximum1093
Range1092
Interquartile range (IQR)50.25

Descriptive statistics

Standard deviation254.68187
Coefficient of variation (CV)2.8456074
Kurtosis16.605296
Mean89.5
Median Absolute Deviation (MAD)10
Skewness4.0234775
Sum1611
Variance64862.853
MonotonicityNot monotonic
2023-01-03T11:55:05.803025image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
1 5
 
0.2%
3 1
 
< 0.1%
23 1
 
< 0.1%
2 1
 
< 0.1%
186 1
 
< 0.1%
87 1
 
< 0.1%
5 1
 
< 0.1%
50 1
 
< 0.1%
58 1
 
< 0.1%
25 1
 
< 0.1%
Other values (4) 4
 
0.1%
(Missing) 3068
99.4%
ValueCountFrequency (%)
1 5
0.2%
2 1
 
< 0.1%
3 1
 
< 0.1%
5 1
 
< 0.1%
7 1
 
< 0.1%
15 1
 
< 0.1%
23 1
 
< 0.1%
25 1
 
< 0.1%
50 1
 
< 0.1%
52 1
 
< 0.1%
ValueCountFrequency (%)
1093 1
< 0.1%
186 1
< 0.1%
87 1
< 0.1%
58 1
< 0.1%
52 1
< 0.1%
50 1
< 0.1%
25 1
< 0.1%
23 1
< 0.1%
15 1
< 0.1%
7 1
< 0.1%

17 Mbps
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing3085
Missing (%)> 99.9%
Memory size24.2 KiB
2.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row2.0

Common Values

ValueCountFrequency (%)
2.0 1
 
< 0.1%
(Missing) 3085
> 99.9%

Length

2023-01-03T11:55:05.872039image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-03T11:55:05.940345image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
2.0 1
100.0%

Most occurring characters

ValueCountFrequency (%)
2 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2
66.7%
Other Punctuation 1
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 1
50.0%
0 1
50.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 1
33.3%
. 1
33.3%
0 1
33.3%

18 Mbps
Real number (ℝ)

Distinct118
Distinct (%)50.9%
Missing2854
Missing (%)92.5%
Infinite0
Infinite (%)0.0%
Mean202.43966
Minimum1
Maximum15630
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.2 KiB
2023-01-03T11:55:06.004428image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q17
median25
Q3130
95-th percentile526.2
Maximum15630
Range15629
Interquartile range (IQR)123

Descriptive statistics

Standard deviation1081.1591
Coefficient of variation (CV)5.3406487
Kurtosis181.9077
Mean202.43966
Median Absolute Deviation (MAD)23
Skewness12.935396
Sum46966
Variance1168905
MonotonicityNot monotonic
2023-01-03T11:55:06.093410image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 17
 
0.6%
4 9
 
0.3%
5 9
 
0.3%
2 9
 
0.3%
7 9
 
0.3%
6 7
 
0.2%
3 6
 
0.2%
12 5
 
0.2%
11 4
 
0.1%
15 4
 
0.1%
Other values (108) 153
 
5.0%
(Missing) 2854
92.5%
ValueCountFrequency (%)
1 17
0.6%
2 9
0.3%
3 6
 
0.2%
4 9
0.3%
5 9
0.3%
6 7
0.2%
7 9
0.3%
8 3
 
0.1%
9 3
 
0.1%
10 4
 
0.1%
ValueCountFrequency (%)
15630 1
< 0.1%
3771 1
< 0.1%
2953 1
< 0.1%
1866 1
< 0.1%
955 1
< 0.1%
940 1
< 0.1%
876 1
< 0.1%
833 1
< 0.1%
791 1
< 0.1%
718 1
< 0.1%

19 Mbps
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing3085
Missing (%)> 99.9%
Memory size24.2 KiB
1.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row1.0

Common Values

ValueCountFrequency (%)
1.0 1
 
< 0.1%
(Missing) 3085
> 99.9%

Length

2023-01-03T11:55:06.176477image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-03T11:55:06.240613image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0 1
100.0%

Most occurring characters

ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2
66.7%
Other Punctuation 1
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1
50.0%
0 1
50.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

20 Mbps
Real number (ℝ)

Distinct317
Distinct (%)33.5%
Missing2139
Missing (%)69.3%
Infinite0
Infinite (%)0.0%
Mean435.64308
Minimum1
Maximum46004
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.2 KiB
2023-01-03T11:55:06.317487image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median26
Q3116.5
95-th percentile1401.6
Maximum46004
Range46003
Interquartile range (IQR)112.5

Descriptive statistics

Standard deviation2273.8444
Coefficient of variation (CV)5.2195121
Kurtosis192.36659
Mean435.64308
Median Absolute Deviation (MAD)25
Skewness11.941006
Sum412554
Variance5170368.1
MonotonicityNot monotonic
2023-01-03T11:55:06.405722image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 129
 
4.2%
2 65
 
2.1%
5 40
 
1.3%
3 33
 
1.1%
4 26
 
0.8%
6 18
 
0.6%
8 17
 
0.6%
14 16
 
0.5%
30 15
 
0.5%
7 15
 
0.5%
Other values (307) 573
 
18.6%
(Missing) 2139
69.3%
ValueCountFrequency (%)
1 129
4.2%
2 65
2.1%
3 33
 
1.1%
4 26
 
0.8%
5 40
 
1.3%
6 18
 
0.6%
7 15
 
0.5%
8 17
 
0.6%
9 12
 
0.4%
10 6
 
0.2%
ValueCountFrequency (%)
46004 1
< 0.1%
19534 1
< 0.1%
18895 1
< 0.1%
17090 1
< 0.1%
16999 1
< 0.1%
16181 1
< 0.1%
16029 1
< 0.1%
12539 1
< 0.1%
11663 1
< 0.1%
10620 1
< 0.1%

21 Mbps
Categorical

Distinct2
Distinct (%)50.0%
Missing3082
Missing (%)99.9%
Memory size24.2 KiB
1.0
2.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters12
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)25.0%

Sample

1st row1.0
2nd row1.0
3rd row2.0
4th row1.0

Common Values

ValueCountFrequency (%)
1.0 3
 
0.1%
2.0 1
 
< 0.1%
(Missing) 3082
99.9%

Length

2023-01-03T11:55:06.481647image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-03T11:55:06.555847image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0 3
75.0%
2.0 1
 
25.0%

Most occurring characters

ValueCountFrequency (%)
. 4
33.3%
0 4
33.3%
1 3
25.0%
2 1
 
8.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8
66.7%
Other Punctuation 4
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4
50.0%
1 3
37.5%
2 1
 
12.5%
Other Punctuation
ValueCountFrequency (%)
. 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 12
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 4
33.3%
0 4
33.3%
1 3
25.0%
2 1
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 4
33.3%
0 4
33.3%
1 3
25.0%
2 1
 
8.3%

22 Mbps
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing3085
Missing (%)> 99.9%
Memory size24.2 KiB
1.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row1.0

Common Values

ValueCountFrequency (%)
1.0 1
 
< 0.1%
(Missing) 3085
> 99.9%

Length

2023-01-03T11:55:06.616513image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-03T11:55:06.679755image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0 1
100.0%

Most occurring characters

ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2
66.7%
Other Punctuation 1
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1
50.0%
0 1
50.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

23 Mbps
Categorical

MISSING  UNIFORM 

Distinct2
Distinct (%)100.0%
Missing3084
Missing (%)99.9%
Memory size24.2 KiB
15.0
3.0

Length

Max length4
Median length3.5
Mean length3.5
Min length3

Characters and Unicode

Total characters7
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st row15.0
2nd row3.0

Common Values

ValueCountFrequency (%)
15.0 1
 
< 0.1%
3.0 1
 
< 0.1%
(Missing) 3084
99.9%

Length

2023-01-03T11:55:06.918654image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-03T11:55:06.995774image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
15.0 1
50.0%
3.0 1
50.0%

Most occurring characters

ValueCountFrequency (%)
. 2
28.6%
0 2
28.6%
1 1
14.3%
5 1
14.3%
3 1
14.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5
71.4%
Other Punctuation 2
 
28.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2
40.0%
1 1
20.0%
5 1
20.0%
3 1
20.0%
Other Punctuation
ValueCountFrequency (%)
. 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 2
28.6%
0 2
28.6%
1 1
14.3%
5 1
14.3%
3 1
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 2
28.6%
0 2
28.6%
1 1
14.3%
5 1
14.3%
3 1
14.3%

24 Mbps
Real number (ℝ)

Distinct12
Distinct (%)92.3%
Missing3073
Missing (%)99.6%
Infinite0
Infinite (%)0.0%
Mean1547.3077
Minimum2
Maximum13898
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.2 KiB
2023-01-03T11:55:07.057655image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2
Q138
median140
Q3960
95-th percentile7103
Maximum13898
Range13896
Interquartile range (IQR)922

Descriptive statistics

Standard deviation3782.2718
Coefficient of variation (CV)2.4444212
Kurtosis11.775487
Mean1547.3077
Median Absolute Deviation (MAD)138
Skewness3.3841866
Sum20115
Variance14305580
MonotonicityNot monotonic
2023-01-03T11:55:07.120663image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
2 2
 
0.1%
23 1
 
< 0.1%
2573 1
 
< 0.1%
1050 1
 
< 0.1%
140 1
 
< 0.1%
873 1
 
< 0.1%
13898 1
 
< 0.1%
960 1
 
< 0.1%
450 1
 
< 0.1%
39 1
 
< 0.1%
Other values (2) 2
 
0.1%
(Missing) 3073
99.6%
ValueCountFrequency (%)
2 2
0.1%
23 1
< 0.1%
38 1
< 0.1%
39 1
< 0.1%
67 1
< 0.1%
140 1
< 0.1%
450 1
< 0.1%
873 1
< 0.1%
960 1
< 0.1%
1050 1
< 0.1%
ValueCountFrequency (%)
13898 1
< 0.1%
2573 1
< 0.1%
1050 1
< 0.1%
960 1
< 0.1%
873 1
< 0.1%
450 1
< 0.1%
140 1
< 0.1%
67 1
< 0.1%
39 1
< 0.1%
38 1
< 0.1%

25 Mbps
Real number (ℝ)

Distinct332
Distinct (%)56.8%
Missing2502
Missing (%)81.1%
Infinite0
Infinite (%)0.0%
Mean518.44007
Minimum1
Maximum33645
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.2 KiB
2023-01-03T11:55:07.207497image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q116.75
median95.5
Q3340.75
95-th percentile1774.9
Maximum33645
Range33644
Interquartile range (IQR)324

Descriptive statistics

Standard deviation2123.0458
Coefficient of variation (CV)4.0950651
Kurtosis147.76037
Mean518.44007
Median Absolute Deviation (MAD)92.5
Skewness11.167931
Sum302769
Variance4507323.5
MonotonicityNot monotonic
2023-01-03T11:55:07.292738image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 41
 
1.3%
2 17
 
0.6%
3 14
 
0.5%
21 9
 
0.3%
5 9
 
0.3%
16 8
 
0.3%
10 8
 
0.3%
8 7
 
0.2%
13 7
 
0.2%
7 7
 
0.2%
Other values (322) 457
 
14.8%
(Missing) 2502
81.1%
ValueCountFrequency (%)
1 41
1.3%
2 17
0.6%
3 14
 
0.5%
4 6
 
0.2%
5 9
 
0.3%
6 6
 
0.2%
7 7
 
0.2%
8 7
 
0.2%
9 3
 
0.1%
10 8
 
0.3%
ValueCountFrequency (%)
33645 1
< 0.1%
26128 1
< 0.1%
18720 1
< 0.1%
10562 1
< 0.1%
9904 1
< 0.1%
7027 1
< 0.1%
5892 1
< 0.1%
5891 1
< 0.1%
5535 1
< 0.1%
5029 1
< 0.1%

25,1 Mbps
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing3085
Missing (%)> 99.9%
Memory size24.2 KiB
5.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row5.0

Common Values

ValueCountFrequency (%)
5.0 1
 
< 0.1%
(Missing) 3085
> 99.9%

Length

2023-01-03T11:55:07.375588image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-03T11:55:07.438494image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
5.0 1
100.0%

Most occurring characters

ValueCountFrequency (%)
5 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2
66.7%
Other Punctuation 1
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 1
50.0%
0 1
50.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 1
33.3%
. 1
33.3%
0 1
33.3%

25,11 Mbps
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing3085
Missing (%)> 99.9%
Memory size24.2 KiB
1.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row1.0

Common Values

ValueCountFrequency (%)
1.0 1
 
< 0.1%
(Missing) 3085
> 99.9%

Length

2023-01-03T11:55:07.491285image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-03T11:55:07.556194image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0 1
100.0%

Most occurring characters

ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2
66.7%
Other Punctuation 1
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1
50.0%
0 1
50.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

25,5 Mbps
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing3085
Missing (%)> 99.9%
Memory size24.2 KiB
35.0

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters4
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row35.0

Common Values

ValueCountFrequency (%)
35.0 1
 
< 0.1%
(Missing) 3085
> 99.9%

Length

2023-01-03T11:55:07.608520image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-03T11:55:07.674707image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
35.0 1
100.0%

Most occurring characters

ValueCountFrequency (%)
3 1
25.0%
5 1
25.0%
. 1
25.0%
0 1
25.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3
75.0%
Other Punctuation 1
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 1
33.3%
5 1
33.3%
0 1
33.3%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 1
25.0%
5 1
25.0%
. 1
25.0%
0 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 1
25.0%
5 1
25.0%
. 1
25.0%
0 1
25.0%

26 Mbps
Categorical

MISSING  UNIFORM 

Distinct2
Distinct (%)100.0%
Missing3084
Missing (%)99.9%
Memory size24.2 KiB
26.0
10.0

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters8
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st row26.0
2nd row10.0

Common Values

ValueCountFrequency (%)
26.0 1
 
< 0.1%
10.0 1
 
< 0.1%
(Missing) 3084
99.9%

Length

2023-01-03T11:55:07.725846image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-03T11:55:07.797496image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
26.0 1
50.0%
10.0 1
50.0%

Most occurring characters

ValueCountFrequency (%)
0 3
37.5%
. 2
25.0%
2 1
 
12.5%
6 1
 
12.5%
1 1
 
12.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 6
75.0%
Other Punctuation 2
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3
50.0%
2 1
 
16.7%
6 1
 
16.7%
1 1
 
16.7%
Other Punctuation
ValueCountFrequency (%)
. 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 8
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3
37.5%
. 2
25.0%
2 1
 
12.5%
6 1
 
12.5%
1 1
 
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3
37.5%
. 2
25.0%
2 1
 
12.5%
6 1
 
12.5%
1 1
 
12.5%

30 Mbps
Real number (ℝ)

Distinct305
Distinct (%)41.4%
Missing2350
Missing (%)76.2%
Infinite0
Infinite (%)0.0%
Mean1330.5231
Minimum1
Maximum59618
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.2 KiB
2023-01-03T11:55:07.871259image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median18.5
Q3253
95-th percentile8974.75
Maximum59618
Range59617
Interquartile range (IQR)251

Descriptive statistics

Standard deviation4357.8081
Coefficient of variation (CV)3.2752593
Kurtosis64.031527
Mean1330.5231
Median Absolute Deviation (MAD)17.5
Skewness6.7003729
Sum979265
Variance18990492
MonotonicityNot monotonic
2023-01-03T11:55:07.957456image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 136
 
4.4%
2 56
 
1.8%
3 35
 
1.1%
4 24
 
0.8%
9 15
 
0.5%
7 14
 
0.5%
5 13
 
0.4%
6 10
 
0.3%
14 10
 
0.3%
8 9
 
0.3%
Other values (295) 414
 
13.4%
(Missing) 2350
76.2%
ValueCountFrequency (%)
1 136
4.4%
2 56
1.8%
3 35
 
1.1%
4 24
 
0.8%
5 13
 
0.4%
6 10
 
0.3%
7 14
 
0.5%
8 9
 
0.3%
9 15
 
0.5%
10 8
 
0.3%
ValueCountFrequency (%)
59618 1
< 0.1%
42097 1
< 0.1%
37480 1
< 0.1%
25108 1
< 0.1%
24343 1
< 0.1%
22713 1
< 0.1%
21342 1
< 0.1%
19658 1
< 0.1%
17959 1
< 0.1%
17617 1
< 0.1%

31 Mbps
Categorical

MISSING  UNIFORM 

Distinct4
Distinct (%)100.0%
Missing3082
Missing (%)99.9%
Memory size24.2 KiB
266.0
381.0
442.0
308.0

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters20
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)100.0%

Sample

1st row266.0
2nd row381.0
3rd row442.0
4th row308.0

Common Values

ValueCountFrequency (%)
266.0 1
 
< 0.1%
381.0 1
 
< 0.1%
442.0 1
 
< 0.1%
308.0 1
 
< 0.1%
(Missing) 3082
99.9%

Length

2023-01-03T11:55:08.036587image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-03T11:55:08.111785image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
266.0 1
25.0%
381.0 1
25.0%
442.0 1
25.0%
308.0 1
25.0%

Most occurring characters

ValueCountFrequency (%)
0 5
25.0%
. 4
20.0%
2 2
 
10.0%
6 2
 
10.0%
3 2
 
10.0%
8 2
 
10.0%
4 2
 
10.0%
1 1
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 16
80.0%
Other Punctuation 4
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5
31.2%
2 2
 
12.5%
6 2
 
12.5%
3 2
 
12.5%
8 2
 
12.5%
4 2
 
12.5%
1 1
 
6.2%
Other Punctuation
ValueCountFrequency (%)
. 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 20
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 5
25.0%
. 4
20.0%
2 2
 
10.0%
6 2
 
10.0%
3 2
 
10.0%
8 2
 
10.0%
4 2
 
10.0%
1 1
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 5
25.0%
. 4
20.0%
2 2
 
10.0%
6 2
 
10.0%
3 2
 
10.0%
8 2
 
10.0%
4 2
 
10.0%
1 1
 
5.0%

32 Mbps
Categorical

MISSING  UNIFORM 

Distinct2
Distinct (%)100.0%
Missing3084
Missing (%)99.9%
Memory size24.2 KiB
7.0
1.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters6
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st row7.0
2nd row1.0

Common Values

ValueCountFrequency (%)
7.0 1
 
< 0.1%
1.0 1
 
< 0.1%
(Missing) 3084
99.9%

Length

2023-01-03T11:55:08.184022image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-03T11:55:08.263885image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
7.0 1
50.0%
1.0 1
50.0%

Most occurring characters

ValueCountFrequency (%)
. 2
33.3%
0 2
33.3%
7 1
16.7%
1 1
16.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4
66.7%
Other Punctuation 2
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2
50.0%
7 1
25.0%
1 1
25.0%
Other Punctuation
ValueCountFrequency (%)
. 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 2
33.3%
0 2
33.3%
7 1
16.7%
1 1
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 2
33.3%
0 2
33.3%
7 1
16.7%
1 1
16.7%

34 Mbps
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing3085
Missing (%)> 99.9%
Memory size24.2 KiB
2.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row2.0

Common Values

ValueCountFrequency (%)
2.0 1
 
< 0.1%
(Missing) 3085
> 99.9%

Length

2023-01-03T11:55:08.334575image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-03T11:55:08.402772image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
2.0 1
100.0%

Most occurring characters

ValueCountFrequency (%)
2 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2
66.7%
Other Punctuation 1
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 1
50.0%
0 1
50.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 1
33.3%
. 1
33.3%
0 1
33.3%

35 Mbps
Real number (ℝ)

Distinct8
Distinct (%)72.7%
Missing3075
Missing (%)99.6%
Infinite0
Infinite (%)0.0%
Mean121.27273
Minimum1
Maximum762
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.2 KiB
2023-01-03T11:55:08.452879image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.5
Q13.5
median6
Q357.5
95-th percentile594.5
Maximum762
Range761
Interquartile range (IQR)54

Descriptive statistics

Standard deviation247.52539
Coefficient of variation (CV)2.0410639
Kurtosis4.5630466
Mean121.27273
Median Absolute Deviation (MAD)4
Skewness2.2482845
Sum1334
Variance61268.818
MonotonicityNot monotonic
2023-01-03T11:55:08.514672image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
5 2
 
0.1%
9 2
 
0.1%
2 2
 
0.1%
106 1
 
< 0.1%
762 1
 
< 0.1%
6 1
 
< 0.1%
427 1
 
< 0.1%
1 1
 
< 0.1%
(Missing) 3075
99.6%
ValueCountFrequency (%)
1 1
< 0.1%
2 2
0.1%
5 2
0.1%
6 1
< 0.1%
9 2
0.1%
106 1
< 0.1%
427 1
< 0.1%
762 1
< 0.1%
ValueCountFrequency (%)
762 1
< 0.1%
427 1
< 0.1%
106 1
< 0.1%
9 2
0.1%
6 1
< 0.1%
5 2
0.1%
2 2
0.1%
1 1
< 0.1%

36 Mbps
Categorical

MISSING  UNIFORM 

Distinct2
Distinct (%)100.0%
Missing3084
Missing (%)99.9%
Memory size24.2 KiB
82.0
18.0

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters8
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st row82.0
2nd row18.0

Common Values

ValueCountFrequency (%)
82.0 1
 
< 0.1%
18.0 1
 
< 0.1%
(Missing) 3084
99.9%

Length

2023-01-03T11:55:08.590845image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-03T11:55:08.666867image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
82.0 1
50.0%
18.0 1
50.0%

Most occurring characters

ValueCountFrequency (%)
8 2
25.0%
. 2
25.0%
0 2
25.0%
2 1
12.5%
1 1
12.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 6
75.0%
Other Punctuation 2
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 2
33.3%
0 2
33.3%
2 1
16.7%
1 1
16.7%
Other Punctuation
ValueCountFrequency (%)
. 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 8
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 2
25.0%
. 2
25.0%
0 2
25.0%
2 1
12.5%
1 1
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 2
25.0%
. 2
25.0%
0 2
25.0%
2 1
12.5%
1 1
12.5%

38 Mbps
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing3085
Missing (%)> 99.9%
Memory size24.2 KiB
4.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row4.0

Common Values

ValueCountFrequency (%)
4.0 1
 
< 0.1%
(Missing) 3085
> 99.9%

Length

2023-01-03T11:55:08.731118image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-03T11:55:08.797120image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
4.0 1
100.0%

Most occurring characters

ValueCountFrequency (%)
4 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2
66.7%
Other Punctuation 1
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 1
50.0%
0 1
50.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 1
33.3%
. 1
33.3%
0 1
33.3%

39 Mbps
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing3085
Missing (%)> 99.9%
Memory size24.2 KiB
60.0

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters4
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row60.0

Common Values

ValueCountFrequency (%)
60.0 1
 
< 0.1%
(Missing) 3085
> 99.9%

Length

2023-01-03T11:55:08.850611image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-03T11:55:08.917275image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
60.0 1
100.0%

Most occurring characters

ValueCountFrequency (%)
0 2
50.0%
6 1
25.0%
. 1
25.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3
75.0%
Other Punctuation 1
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2
66.7%
6 1
33.3%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2
50.0%
6 1
25.0%
. 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2
50.0%
6 1
25.0%
. 1
25.0%

40 Mbps
Real number (ℝ)

Distinct37
Distinct (%)41.6%
Missing2997
Missing (%)97.1%
Infinite0
Infinite (%)0.0%
Mean68.393258
Minimum1
Maximum1387
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.2 KiB
2023-01-03T11:55:08.976450image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median4
Q328
95-th percentile365.2
Maximum1387
Range1386
Interquartile range (IQR)27

Descriptive statistics

Standard deviation204.61053
Coefficient of variation (CV)2.9916769
Kurtosis23.792613
Mean68.393258
Median Absolute Deviation (MAD)3
Skewness4.6156229
Sum6087
Variance41865.469
MonotonicityNot monotonic
2023-01-03T11:55:09.053638image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
1 28
 
0.9%
2 10
 
0.3%
3 5
 
0.2%
8 4
 
0.1%
4 4
 
0.1%
6 3
 
0.1%
10 2
 
0.1%
39 2
 
0.1%
14 2
 
0.1%
5 2
 
0.1%
Other values (27) 27
 
0.9%
(Missing) 2997
97.1%
ValueCountFrequency (%)
1 28
0.9%
2 10
 
0.3%
3 5
 
0.2%
4 4
 
0.1%
5 2
 
0.1%
6 3
 
0.1%
7 1
 
< 0.1%
8 4
 
0.1%
9 1
 
< 0.1%
10 2
 
0.1%
ValueCountFrequency (%)
1387 1
< 0.1%
924 1
< 0.1%
796 1
< 0.1%
421 1
< 0.1%
368 1
< 0.1%
361 1
< 0.1%
280 1
< 0.1%
272 1
< 0.1%
165 1
< 0.1%
127 1
< 0.1%

41 Mbps
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing3085
Missing (%)> 99.9%
Memory size24.2 KiB
9.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row9.0

Common Values

ValueCountFrequency (%)
9.0 1
 
< 0.1%
(Missing) 3085
> 99.9%

Length

2023-01-03T11:55:09.130609image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-03T11:55:09.195867image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
9.0 1
100.0%

Most occurring characters

ValueCountFrequency (%)
9 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2
66.7%
Other Punctuation 1
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 1
50.0%
0 1
50.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
9 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 1
33.3%
. 1
33.3%
0 1
33.3%

45 Mbps
Categorical

MISSING  UNIFORM 

Distinct3
Distinct (%)100.0%
Missing3083
Missing (%)99.9%
Memory size24.2 KiB
1.0
68.0
103.0

Length

Max length5
Median length4
Mean length4
Min length3

Characters and Unicode

Total characters12
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)100.0%

Sample

1st row1.0
2nd row68.0
3rd row103.0

Common Values

ValueCountFrequency (%)
1.0 1
 
< 0.1%
68.0 1
 
< 0.1%
103.0 1
 
< 0.1%
(Missing) 3083
99.9%

Length

2023-01-03T11:55:09.253507image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-03T11:55:09.338677image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0 1
33.3%
68.0 1
33.3%
103.0 1
33.3%

Most occurring characters

ValueCountFrequency (%)
0 4
33.3%
. 3
25.0%
1 2
16.7%
6 1
 
8.3%
8 1
 
8.3%
3 1
 
8.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9
75.0%
Other Punctuation 3
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4
44.4%
1 2
22.2%
6 1
 
11.1%
8 1
 
11.1%
3 1
 
11.1%
Other Punctuation
ValueCountFrequency (%)
. 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 12
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4
33.3%
. 3
25.0%
1 2
16.7%
6 1
 
8.3%
8 1
 
8.3%
3 1
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4
33.3%
. 3
25.0%
1 2
16.7%
6 1
 
8.3%
8 1
 
8.3%
3 1
 
8.3%

46 Mbps
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing3085
Missing (%)> 99.9%
Memory size24.2 KiB
6.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row6.0

Common Values

ValueCountFrequency (%)
6.0 1
 
< 0.1%
(Missing) 3085
> 99.9%

Length

2023-01-03T11:55:09.402761image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-03T11:55:09.469504image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
6.0 1
100.0%

Most occurring characters

ValueCountFrequency (%)
6 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2
66.7%
Other Punctuation 1
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
6 1
50.0%
0 1
50.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
6 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6 1
33.3%
. 1
33.3%
0 1
33.3%

49 Mbps
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing3085
Missing (%)> 99.9%
Memory size24.2 KiB
1.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row1.0

Common Values

ValueCountFrequency (%)
1.0 1
 
< 0.1%
(Missing) 3085
> 99.9%

Length

2023-01-03T11:55:09.522635image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-03T11:55:09.586869image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0 1
100.0%

Most occurring characters

ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2
66.7%
Other Punctuation 1
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1
50.0%
0 1
50.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

50 Mbps
Real number (ℝ)

Distinct421
Distinct (%)68.9%
Missing2475
Missing (%)80.2%
Infinite0
Infinite (%)0.0%
Mean4006.2668
Minimum1
Maximum420771
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.2 KiB
2023-01-03T11:55:09.655694image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q118
median408
Q32532
95-th percentile11759
Maximum420771
Range420770
Interquartile range (IQR)2514

Descriptive statistics

Standard deviation20991.65
Coefficient of variation (CV)5.2397034
Kurtosis276.39352
Mean4006.2668
Median Absolute Deviation (MAD)406
Skewness15.265579
Sum2447829
Variance4.4064936 × 108
MonotonicityNot monotonic
2023-01-03T11:55:09.740641image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 50
 
1.6%
2 19
 
0.6%
3 11
 
0.4%
8 10
 
0.3%
4 10
 
0.3%
6 9
 
0.3%
5 9
 
0.3%
12 8
 
0.3%
9 7
 
0.2%
41 5
 
0.2%
Other values (411) 473
 
15.3%
(Missing) 2475
80.2%
ValueCountFrequency (%)
1 50
1.6%
2 19
 
0.6%
3 11
 
0.4%
4 10
 
0.3%
5 9
 
0.3%
6 9
 
0.3%
7 4
 
0.1%
8 10
 
0.3%
9 7
 
0.2%
10 3
 
0.1%
ValueCountFrequency (%)
420771 1
< 0.1%
225133 1
< 0.1%
97227 1
< 0.1%
95939 1
< 0.1%
78241 1
< 0.1%
67264 1
< 0.1%
57863 1
< 0.1%
45006 1
< 0.1%
44775 1
< 0.1%
35211 1
< 0.1%

55 Mbps
Categorical

Distinct3
Distinct (%)75.0%
Missing3082
Missing (%)99.9%
Memory size24.2 KiB
1.0
2.0
57.0

Length

Max length4
Median length3
Mean length3.25
Min length3

Characters and Unicode

Total characters13
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)50.0%

Sample

1st row2.0
2nd row57.0
3rd row1.0
4th row1.0

Common Values

ValueCountFrequency (%)
1.0 2
 
0.1%
2.0 1
 
< 0.1%
57.0 1
 
< 0.1%
(Missing) 3082
99.9%

Length

2023-01-03T11:55:09.818788image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-03T11:55:09.896445image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0 2
50.0%
2.0 1
25.0%
57.0 1
25.0%

Most occurring characters

ValueCountFrequency (%)
. 4
30.8%
0 4
30.8%
1 2
15.4%
2 1
 
7.7%
5 1
 
7.7%
7 1
 
7.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9
69.2%
Other Punctuation 4
30.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4
44.4%
1 2
22.2%
2 1
 
11.1%
5 1
 
11.1%
7 1
 
11.1%
Other Punctuation
ValueCountFrequency (%)
. 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 13
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 4
30.8%
0 4
30.8%
1 2
15.4%
2 1
 
7.7%
5 1
 
7.7%
7 1
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 4
30.8%
0 4
30.8%
1 2
15.4%
2 1
 
7.7%
5 1
 
7.7%
7 1
 
7.7%

58 Mbps
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing3085
Missing (%)> 99.9%
Memory size24.2 KiB
2.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row2.0

Common Values

ValueCountFrequency (%)
2.0 1
 
< 0.1%
(Missing) 3085
> 99.9%

Length

2023-01-03T11:55:09.961403image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-03T11:55:10.026940image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
2.0 1
100.0%

Most occurring characters

ValueCountFrequency (%)
2 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2
66.7%
Other Punctuation 1
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 1
50.0%
0 1
50.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 1
33.3%
. 1
33.3%
0 1
33.3%

59 Mbps
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing3085
Missing (%)> 99.9%
Memory size24.2 KiB
59.0

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters4
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row59.0

Common Values

ValueCountFrequency (%)
59.0 1
 
< 0.1%
(Missing) 3085
> 99.9%

Length

2023-01-03T11:55:10.078643image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-03T11:55:10.145659image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
59.0 1
100.0%

Most occurring characters

ValueCountFrequency (%)
5 1
25.0%
9 1
25.0%
. 1
25.0%
0 1
25.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3
75.0%
Other Punctuation 1
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 1
33.3%
9 1
33.3%
0 1
33.3%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 1
25.0%
9 1
25.0%
. 1
25.0%
0 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 1
25.0%
9 1
25.0%
. 1
25.0%
0 1
25.0%

60 Mbps
Real number (ℝ)

Distinct237
Distinct (%)92.9%
Missing2831
Missing (%)91.7%
Infinite0
Infinite (%)0.0%
Mean2088.2627
Minimum1
Maximum100334
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.2 KiB
2023-01-03T11:55:10.211613image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8.1
Q1207.5
median938
Q32141.5
95-th percentile6727
Maximum100334
Range100333
Interquartile range (IQR)1934

Descriptive statistics

Standard deviation6627.5138
Coefficient of variation (CV)3.1736973
Kurtosis191.8274
Mean2088.2627
Median Absolute Deviation (MAD)791
Skewness13.039087
Sum532507
Variance43923940
MonotonicityNot monotonic
2023-01-03T11:55:10.304976image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19 4
 
0.1%
2 4
 
0.1%
1 3
 
0.1%
10 3
 
0.1%
4 3
 
0.1%
612 2
 
0.1%
30 2
 
0.1%
6 2
 
0.1%
859 2
 
0.1%
54 2
 
0.1%
Other values (227) 228
 
7.4%
(Missing) 2831
91.7%
ValueCountFrequency (%)
1 3
0.1%
2 4
0.1%
3 1
 
< 0.1%
4 3
0.1%
6 2
0.1%
9 1
 
< 0.1%
10 3
0.1%
13 1
 
< 0.1%
14 1
 
< 0.1%
15 1
 
< 0.1%
ValueCountFrequency (%)
100334 1
< 0.1%
16631 1
< 0.1%
12952 1
< 0.1%
12708 1
< 0.1%
11460 1
< 0.1%
10179 1
< 0.1%
9529 1
< 0.1%
8685 1
< 0.1%
8371 1
< 0.1%
8055 1
< 0.1%

61 Mbps
Categorical

MISSING  UNIFORM 

Distinct3
Distinct (%)100.0%
Missing3083
Missing (%)99.9%
Memory size24.2 KiB
31.0
19.0
28.0

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters12
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)100.0%

Sample

1st row31.0
2nd row19.0
3rd row28.0

Common Values

ValueCountFrequency (%)
31.0 1
 
< 0.1%
19.0 1
 
< 0.1%
28.0 1
 
< 0.1%
(Missing) 3083
99.9%

Length

2023-01-03T11:55:10.538850image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-03T11:55:10.610620image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
31.0 1
33.3%
19.0 1
33.3%
28.0 1
33.3%

Most occurring characters

ValueCountFrequency (%)
. 3
25.0%
0 3
25.0%
1 2
16.7%
3 1
 
8.3%
9 1
 
8.3%
2 1
 
8.3%
8 1
 
8.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9
75.0%
Other Punctuation 3
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3
33.3%
1 2
22.2%
3 1
 
11.1%
9 1
 
11.1%
2 1
 
11.1%
8 1
 
11.1%
Other Punctuation
ValueCountFrequency (%)
. 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 12
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 3
25.0%
0 3
25.0%
1 2
16.7%
3 1
 
8.3%
9 1
 
8.3%
2 1
 
8.3%
8 1
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 3
25.0%
0 3
25.0%
1 2
16.7%
3 1
 
8.3%
9 1
 
8.3%
2 1
 
8.3%
8 1
 
8.3%

62 Mbps
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing3085
Missing (%)> 99.9%
Memory size24.2 KiB
2.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row2.0

Common Values

ValueCountFrequency (%)
2.0 1
 
< 0.1%
(Missing) 3085
> 99.9%

Length

2023-01-03T11:55:10.672774image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-03T11:55:10.736674image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
2.0 1
100.0%

Most occurring characters

ValueCountFrequency (%)
2 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2
66.7%
Other Punctuation 1
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 1
50.0%
0 1
50.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 1
33.3%
. 1
33.3%
0 1
33.3%

64 Mbps
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing3085
Missing (%)> 99.9%
Memory size24.2 KiB
8.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row8.0

Common Values

ValueCountFrequency (%)
8.0 1
 
< 0.1%
(Missing) 3085
> 99.9%

Length

2023-01-03T11:55:10.789499image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-03T11:55:10.857367image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
8.0 1
100.0%

Most occurring characters

ValueCountFrequency (%)
8 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2
66.7%
Other Punctuation 1
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 1
50.0%
0 1
50.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 1
33.3%
. 1
33.3%
0 1
33.3%

65 Mbps
Categorical

MISSING  UNIFORM 

Distinct2
Distinct (%)100.0%
Missing3084
Missing (%)99.9%
Memory size24.2 KiB
1.0
5.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters6
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st row1.0
2nd row5.0

Common Values

ValueCountFrequency (%)
1.0 1
 
< 0.1%
5.0 1
 
< 0.1%
(Missing) 3084
99.9%

Length

2023-01-03T11:55:10.907463image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-03T11:55:10.977198image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0 1
50.0%
5.0 1
50.0%

Most occurring characters

ValueCountFrequency (%)
. 2
33.3%
0 2
33.3%
1 1
16.7%
5 1
16.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4
66.7%
Other Punctuation 2
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2
50.0%
1 1
25.0%
5 1
25.0%
Other Punctuation
ValueCountFrequency (%)
. 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 2
33.3%
0 2
33.3%
1 1
16.7%
5 1
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 2
33.3%
0 2
33.3%
1 1
16.7%
5 1
16.7%

66 Mbps
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing3085
Missing (%)> 99.9%
Memory size24.2 KiB
1.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row1.0

Common Values

ValueCountFrequency (%)
1.0 1
 
< 0.1%
(Missing) 3085
> 99.9%

Length

2023-01-03T11:55:11.038443image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-03T11:55:11.103218image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0 1
100.0%

Most occurring characters

ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2
66.7%
Other Punctuation 1
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1
50.0%
0 1
50.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

70 Mbps
Real number (ℝ)

Distinct7
Distinct (%)70.0%
Missing3076
Missing (%)99.7%
Infinite0
Infinite (%)0.0%
Mean317.1
Minimum1
Maximum2827
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.2 KiB
2023-01-03T11:55:11.149868image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11.25
median2.5
Q391.75
95-th percentile1622.8
Maximum2827
Range2826
Interquartile range (IQR)90.5

Descriptive statistics

Standard deviation883.5516
Coefficient of variation (CV)2.7863501
Kurtosis9.8962773
Mean317.1
Median Absolute Deviation (MAD)1.5
Skewness3.1402995
Sum3171
Variance780663.43
MonotonicityNot monotonic
2023-01-03T11:55:11.200370image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1 3
 
0.1%
2 2
 
0.1%
92 1
 
< 0.1%
91 1
 
< 0.1%
151 1
 
< 0.1%
2827 1
 
< 0.1%
3 1
 
< 0.1%
(Missing) 3076
99.7%
ValueCountFrequency (%)
1 3
0.1%
2 2
0.1%
3 1
 
< 0.1%
91 1
 
< 0.1%
92 1
 
< 0.1%
151 1
 
< 0.1%
2827 1
 
< 0.1%
ValueCountFrequency (%)
2827 1
 
< 0.1%
151 1
 
< 0.1%
92 1
 
< 0.1%
91 1
 
< 0.1%
3 1
 
< 0.1%
2 2
0.1%
1 3
0.1%

75 Mbps
Real number (ℝ)

Distinct198
Distinct (%)88.0%
Missing2861
Missing (%)92.7%
Infinite0
Infinite (%)0.0%
Mean589.71111
Minimum1
Maximum31684
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.2 KiB
2023-01-03T11:55:11.276688image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8.2
Q189
median294
Q3536
95-th percentile1735.4
Maximum31684
Range31683
Interquartile range (IQR)447

Descriptive statistics

Standard deviation2160.1592
Coefficient of variation (CV)3.6630804
Kurtosis193.87303
Mean589.71111
Median Absolute Deviation (MAD)216
Skewness13.472069
Sum132685
Variance4666287.9
MonotonicityNot monotonic
2023-01-03T11:55:11.364950image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
72 5
 
0.2%
1 4
 
0.1%
2 3
 
0.1%
211 3
 
0.1%
12 2
 
0.1%
60 2
 
0.1%
20 2
 
0.1%
276 2
 
0.1%
86 2
 
0.1%
311 2
 
0.1%
Other values (188) 198
 
6.4%
(Missing) 2861
92.7%
ValueCountFrequency (%)
1 4
0.1%
2 3
0.1%
3 1
 
< 0.1%
5 1
 
< 0.1%
6 1
 
< 0.1%
7 1
 
< 0.1%
8 1
 
< 0.1%
9 1
 
< 0.1%
12 2
0.1%
13 1
 
< 0.1%
ValueCountFrequency (%)
31684 1
< 0.1%
3704 1
< 0.1%
3233 1
< 0.1%
2989 1
< 0.1%
2646 1
< 0.1%
2399 1
< 0.1%
2327 1
< 0.1%
2008 1
< 0.1%
1970 1
< 0.1%
1862 1
< 0.1%

78 Mbps
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing3085
Missing (%)> 99.9%
Memory size24.2 KiB
4.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row4.0

Common Values

ValueCountFrequency (%)
4.0 1
 
< 0.1%
(Missing) 3085
> 99.9%

Length

2023-01-03T11:55:11.443835image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-03T11:55:11.508509image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
4.0 1
100.0%

Most occurring characters

ValueCountFrequency (%)
4 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2
66.7%
Other Punctuation 1
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 1
50.0%
0 1
50.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 1
33.3%
. 1
33.3%
0 1
33.3%

80 Mbps
Real number (ℝ)

Distinct10
Distinct (%)90.9%
Missing3075
Missing (%)99.6%
Infinite0
Infinite (%)0.0%
Mean171.36364
Minimum3
Maximum952
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.2 KiB
2023-01-03T11:55:11.558621image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile3
Q16
median24
Q396.5
95-th percentile802.5
Maximum952
Range949
Interquartile range (IQR)90.5

Descriptive statistics

Standard deviation321.16546
Coefficient of variation (CV)1.8741751
Kurtosis3.240835
Mean171.36364
Median Absolute Deviation (MAD)21
Skewness2.0579875
Sum1885
Variance103147.25
MonotonicityNot monotonic
2023-01-03T11:55:11.619673image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
3 2
 
0.1%
9 1
 
< 0.1%
73 1
 
< 0.1%
4 1
 
< 0.1%
120 1
 
< 0.1%
8 1
 
< 0.1%
24 1
 
< 0.1%
36 1
 
< 0.1%
952 1
 
< 0.1%
653 1
 
< 0.1%
(Missing) 3075
99.6%
ValueCountFrequency (%)
3 2
0.1%
4 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
24 1
< 0.1%
36 1
< 0.1%
73 1
< 0.1%
120 1
< 0.1%
653 1
< 0.1%
952 1
< 0.1%
ValueCountFrequency (%)
952 1
< 0.1%
653 1
< 0.1%
120 1
< 0.1%
73 1
< 0.1%
36 1
< 0.1%
24 1
< 0.1%
9 1
< 0.1%
8 1
< 0.1%
4 1
< 0.1%
3 2
0.1%

82 Mbps
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing3085
Missing (%)> 99.9%
Memory size24.2 KiB
1.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row1.0

Common Values

ValueCountFrequency (%)
1.0 1
 
< 0.1%
(Missing) 3085
> 99.9%

Length

2023-01-03T11:55:11.681862image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-03T11:55:11.746208image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0 1
100.0%

Most occurring characters

ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2
66.7%
Other Punctuation 1
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1
50.0%
0 1
50.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

83 Mbps
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing3085
Missing (%)> 99.9%
Memory size24.2 KiB
2.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row2.0

Common Values

ValueCountFrequency (%)
2.0 1
 
< 0.1%
(Missing) 3085
> 99.9%

Length

2023-01-03T11:55:11.799295image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-03T11:55:11.864866image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
2.0 1
100.0%

Most occurring characters

ValueCountFrequency (%)
2 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2
66.7%
Other Punctuation 1
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 1
50.0%
0 1
50.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 1
33.3%
. 1
33.3%
0 1
33.3%

85 Mbps
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing3085
Missing (%)> 99.9%
Memory size24.2 KiB
14.0

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters4
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row14.0

Common Values

ValueCountFrequency (%)
14.0 1
 
< 0.1%
(Missing) 3085
> 99.9%

Length

2023-01-03T11:55:11.917786image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-03T11:55:11.980764image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
14.0 1
100.0%

Most occurring characters

ValueCountFrequency (%)
1 1
25.0%
4 1
25.0%
. 1
25.0%
0 1
25.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3
75.0%
Other Punctuation 1
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1
33.3%
4 1
33.3%
0 1
33.3%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1
25.0%
4 1
25.0%
. 1
25.0%
0 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1
25.0%
4 1
25.0%
. 1
25.0%
0 1
25.0%

90 Mbps
Categorical

MISSING  UNIFORM 

Distinct2
Distinct (%)100.0%
Missing3084
Missing (%)99.9%
Memory size24.2 KiB
3.0
1.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters6
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st row3.0
2nd row1.0

Common Values

ValueCountFrequency (%)
3.0 1
 
< 0.1%
1.0 1
 
< 0.1%
(Missing) 3084
99.9%

Length

2023-01-03T11:55:12.034882image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-03T11:55:12.109361image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
3.0 1
50.0%
1.0 1
50.0%

Most occurring characters

ValueCountFrequency (%)
. 2
33.3%
0 2
33.3%
3 1
16.7%
1 1
16.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4
66.7%
Other Punctuation 2
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2
50.0%
3 1
25.0%
1 1
25.0%
Other Punctuation
ValueCountFrequency (%)
. 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 2
33.3%
0 2
33.3%
3 1
16.7%
1 1
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 2
33.3%
0 2
33.3%
3 1
16.7%
1 1
16.7%

92 Mbps
Categorical

MISSING  UNIFORM 

Distinct2
Distinct (%)100.0%
Missing3084
Missing (%)99.9%
Memory size24.2 KiB
4.0
2.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters6
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st row4.0
2nd row2.0

Common Values

ValueCountFrequency (%)
4.0 1
 
< 0.1%
2.0 1
 
< 0.1%
(Missing) 3084
99.9%

Length

2023-01-03T11:55:12.168534image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-03T11:55:12.238816image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
4.0 1
50.0%
2.0 1
50.0%

Most occurring characters

ValueCountFrequency (%)
. 2
33.3%
0 2
33.3%
4 1
16.7%
2 1
16.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4
66.7%
Other Punctuation 2
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2
50.0%
4 1
25.0%
2 1
25.0%
Other Punctuation
ValueCountFrequency (%)
. 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 2
33.3%
0 2
33.3%
4 1
16.7%
2 1
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 2
33.3%
0 2
33.3%
4 1
16.7%
2 1
16.7%

95 Mbps
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing3085
Missing (%)> 99.9%
Memory size24.2 KiB
1.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row1.0

Common Values

ValueCountFrequency (%)
1.0 1
 
< 0.1%
(Missing) 3085
> 99.9%

Length

2023-01-03T11:55:12.300315image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-03T11:55:12.362419image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0 1
100.0%

Most occurring characters

ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2
66.7%
Other Punctuation 1
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1
50.0%
0 1
50.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

100 Mbps
Real number (ℝ)

Distinct417
Distinct (%)76.9%
Missing2544
Missing (%)82.4%
Infinite0
Infinite (%)0.0%
Mean3520.0941
Minimum0
Maximum427452
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size24.2 KiB
2023-01-03T11:55:12.429348image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q153
median778
Q32725.5
95-th percentile9203.65
Maximum427452
Range427452
Interquartile range (IQR)2672.5

Descriptive statistics

Standard deviation19758.208
Coefficient of variation (CV)5.6129773
Kurtosis395.68917
Mean3520.0941
Median Absolute Deviation (MAD)773
Skewness18.841313
Sum1907891
Variance3.903868 × 108
MonotonicityNot monotonic
2023-01-03T11:55:12.515828image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 48
 
1.6%
2 17
 
0.6%
5 9
 
0.3%
3 6
 
0.2%
15 5
 
0.2%
4 4
 
0.1%
6 4
 
0.1%
8 4
 
0.1%
216 3
 
0.1%
13 3
 
0.1%
Other values (407) 439
 
14.2%
(Missing) 2544
82.4%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 48
1.6%
2 17
 
0.6%
3 6
 
0.2%
4 4
 
0.1%
5 9
 
0.3%
6 4
 
0.1%
7 3
 
0.1%
8 4
 
0.1%
9 2
 
0.1%
ValueCountFrequency (%)
427452 1
< 0.1%
119847 1
< 0.1%
63002 1
< 0.1%
54420 1
< 0.1%
53759 1
< 0.1%
42076 1
< 0.1%
24879 1
< 0.1%
22120 1
< 0.1%
20090 1
< 0.1%
19997 1
< 0.1%

Interactions

2023-01-03T11:54:55.021971image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:53:24.969759image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:53:27.570675image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:53:30.147865image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:53:32.690806image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:53:35.150522image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:53:37.875378image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:53:40.511592image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:53:42.781818image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:53:45.455955image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:53:48.064513image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:53:50.830549image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:53:53.381663image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:53:55.915034image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:53:58.554284image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:54:00.963543image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:54:03.658352image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:54:06.300266image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:54:08.830263image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:54:11.378805image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:54:14.069505image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:54:16.376193image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:54:19.002014image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:54:21.709703image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:54:24.170507image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:54:26.991534image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:54:29.609339image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:54:32.005223image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:54:34.737984image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:54:37.245189image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:54:39.788698image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:54:42.326837image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:54:44.729215image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:54:47.298660image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:54:49.841647image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:54:52.463355image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:54:55.091839image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:53:25.061275image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:53:27.638537image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:53:30.222923image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:53:32.765119image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:53:35.222641image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:53:37.952860image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:53:40.577589image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:53:42.856399image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:53:45.532783image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:53:48.140475image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:53:50.900531image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:53:53.454324image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:53:55.990685image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:53:58.619193image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:54:01.038187image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:54:03.728497image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:54:06.376024image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:54:08.895850image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:54:11.456199image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:54:14.130772image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:54:16.445956image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
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2023-01-03T11:54:37.110820image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:54:39.656867image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:54:42.201728image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:54:44.602937image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:54:47.167015image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:54:49.697975image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:54:52.324032image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:54:54.909090image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:54:57.522362image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:53:27.505215image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:53:30.086707image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:53:32.625466image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:53:35.091161image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:53:37.809823image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:53:40.449686image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:53:42.718650image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:53:45.393392image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:53:47.994712image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:53:50.760524image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:53:53.322337image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:53:55.850261image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:53:58.490582image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:54:00.904303image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:54:03.589449image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:54:06.237837image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:54:08.765329image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:54:11.315482image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:54:14.002521image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:54:16.307481image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:54:18.936399image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:54:21.638149image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:54:24.110191image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:54:26.920563image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:54:29.536942image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:54:31.940861image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:54:34.667310image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:54:37.172500image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:54:39.726772image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:54:42.264256image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:54:44.661517image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:54:47.230934image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:54:49.769727image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:54:52.394947image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:54:54.963735image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Missing values

2023-01-03T11:54:57.749232image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-01-03T11:54:58.249874image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-01-03T11:54:59.258497image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

ProvinciaPartidoLocalidadLink IndecOtros0,256 Mbps0,375 Mbps0,5 Mbps0,512 Mbps0,625 Mbps0,75 Mbps1 Mbps1,25 Mbps1,5 Mbps2 Mbps2,2 Mbps2,5 Mbps3 Mbps3,3 Mbps3,5 Mbps4 Mbps4,5 Mbps5 Mbps6 Mbps6,4 Mbps7 Mbps7,5 Mbps8 Mbps9 Mbps10 Mbps11 Mbps12 Mbps13 Mbps14 Mbps15 Mbps16 Mbps17 Mbps18 Mbps19 Mbps20 Mbps21 Mbps22 Mbps23 Mbps24 Mbps25 Mbps25,1 Mbps25,11 Mbps25,5 Mbps26 Mbps30 Mbps31 Mbps32 Mbps34 Mbps35 Mbps36 Mbps38 Mbps39 Mbps40 Mbps41 Mbps45 Mbps46 Mbps49 Mbps50 Mbps55 Mbps58 Mbps59 Mbps60 Mbps61 Mbps62 Mbps64 Mbps65 Mbps66 Mbps70 Mbps75 Mbps78 Mbps80 Mbps82 Mbps83 Mbps85 Mbps90 Mbps92 Mbps95 Mbps100 Mbps
0BUENOS AIRES25 de Mayo25 de Mayo6854100NaNNaNNaN2.0NaNNaN21.0NaNNaNNaNNaNNaNNaN85.0NaN156.0NaNNaNNaN173.0NaNNaNNaN314.0NaN144.0NaN4134.0NaNNaN430.0NaNNaNNaNNaN604.0NaNNaNNaNNaN211.0NaNNaNNaNNaN63.0NaNNaNNaNNaNNaNNaNNaN37.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN9.0NaNNaNNaNNaNNaNNaNNaN
1BUENOS AIRES25 de MayoAgustín Mosconi6854010NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN45.0NaNNaNNaNNaNNaN13.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
2BUENOS AIRES25 de MayoDel Valle6854020NaNNaNNaN1.0NaNNaNNaNNaNNaNNaNNaNNaNNaN181.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN10.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
3BUENOS AIRES25 de MayoErnestina6854030NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN66.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
4BUENOS AIRES25 de MayoGobernador Ugarte6854040NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN106.0NaNNaN3.0NaNNaN56.0NaN7.03.0NaNNaNNaN4.0NaN1.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
5BUENOS AIRES25 de MayoLucas Monteverde6854050NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN6.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
6BUENOS AIRES25 de MayoNorberto de la Riestra6854060NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN224.0NaNNaN53.0NaNNaN18.0NaN29.0800.0NaNNaNNaN2.0NaN1.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
7BUENOS AIRES25 de MayoPedernales6854070NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN395.0NaNNaN173.0NaNNaN2.0NaNNaNNaNNaNNaN2.0NaNNaNNaNNaN3.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
8BUENOS AIRES25 de MayoSan Enrique6854080NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN85.0NaNNaNNaNNaNNaN18.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
9BUENOS AIRES25 de MayoValdés6854090NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN33.0NaNNaNNaNNaNNaN133.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
ProvinciaPartidoLocalidadLink IndecOtros0,256 Mbps0,375 Mbps0,5 Mbps0,512 Mbps0,625 Mbps0,75 Mbps1 Mbps1,25 Mbps1,5 Mbps2 Mbps2,2 Mbps2,5 Mbps3 Mbps3,3 Mbps3,5 Mbps4 Mbps4,5 Mbps5 Mbps6 Mbps6,4 Mbps7 Mbps7,5 Mbps8 Mbps9 Mbps10 Mbps11 Mbps12 Mbps13 Mbps14 Mbps15 Mbps16 Mbps17 Mbps18 Mbps19 Mbps20 Mbps21 Mbps22 Mbps23 Mbps24 Mbps25 Mbps25,1 Mbps25,11 Mbps25,5 Mbps26 Mbps30 Mbps31 Mbps32 Mbps34 Mbps35 Mbps36 Mbps38 Mbps39 Mbps40 Mbps41 Mbps45 Mbps46 Mbps49 Mbps50 Mbps55 Mbps58 Mbps59 Mbps60 Mbps61 Mbps62 Mbps64 Mbps65 Mbps66 Mbps70 Mbps75 Mbps78 Mbps80 Mbps82 Mbps83 Mbps85 Mbps90 Mbps92 Mbps95 Mbps100 Mbps
3076TUCUMANTafí del ValleEl Mollar90098030NaNNaNNaN1.0NaNNaNNaN5.0NaNNaNNaNNaNNaN4.0NaNNaNNaNNaN2.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
3077TUCUMANTafí del ValleTafí del Valle90098040NaNNaNNaN1.0NaNNaNNaN9.0NaNNaNNaNNaNNaN70.0NaNNaNNaNNaNNaN44.0NaNNaNNaNNaNNaN64.0NaNNaNNaNNaN4.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN10.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
3078TUCUMANTafí ViejoBarrio Lomas de Tafí90105020NaNNaNNaNNaNNaNNaNNaN2.0NaNNaNNaNNaNNaN2.0NaNNaNNaNNaNNaN21.0NaNNaNNaNNaNNaN201.0NaNNaNNaNNaN12.0NaNNaNNaNNaN186.0NaNNaNNaNNaNNaNNaNNaNNaNNaN2.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
3079TUCUMANTafí ViejoBarrio Mutual San Martín90105030NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN4.0NaNNaNNaNNaNNaN21.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
3080TUCUMANTafí ViejoEl Cadillal901050701.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
3081TUCUMANTafí ViejoTafí Viejo901050801.0NaNNaNNaNNaNNaNNaN50.0NaNNaN1.0NaNNaN134.0NaNNaN1.0NaN554.0879.0NaNNaNNaNNaNNaN4522.0NaNNaNNaNNaN168.0NaNNaNNaN1.02283.0NaNNaNNaNNaN131.0NaNNaNNaNNaN76.0NaNNaNNaN1.0NaNNaNNaNNaNNaNNaNNaNNaN1709.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN982.0
3082TUCUMANTrancasSan Pedro de Colalao90112020NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN38.0NaN83.0NaN25.0NaN88.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
3083TUCUMANTrancasVilla de Trancas90112030NaNNaNNaNNaNNaNNaNNaN2.0NaNNaN1.0NaNNaN9.0NaNNaNNaNNaNNaN76.0NaN15.0NaN15.0NaN188.0NaNNaNNaNNaN2.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
3084TUCUMANYerba BuenaVilla Carmela90119020NaNNaNNaNNaNNaNNaNNaN11.0NaNNaNNaNNaNNaN184.0NaNNaNNaNNaN97.0416.0NaNNaNNaNNaNNaN1319.0NaNNaNNaNNaN95.0NaNNaNNaNNaN38.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
3085TUCUMANYerba BuenaYerba Buena - Marcos Paz90119030176.0NaNNaNNaNNaNNaNNaN15.0NaNNaNNaNNaNNaN14.0NaNNaNNaNNaNNaN9.0NaNNaNNaNNaNNaN7.0NaNNaNNaNNaN2.0NaNNaNNaNNaN2011.0NaNNaNNaNNaN248.0NaNNaNNaNNaN29.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN5406.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN5860.0